Identifying nucleotides by determining phasing

ABSTRACT

Methods and systems for analysis of image data generated from various reference points. Particularly, the methods and systems provided are useful for real time analysis of image and sequence data generated during DNA sequencing methodologies.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/445,994 filed Aug. 26, 2021, which is a continuation of U.S.application Ser. No. 17/157,622 filed Jan. 25, 2021, now U.S. Pat. No.11,605,165 issued Mar. 14, 2023, which is a continuation of U.S.application Ser. No. 16/378,894 filed Apr. 9, 2019, which is acontinuation of U.S. application Ser. No. 15/354,540 filed Nov. 17,2016, now U.S. Pat. No. 10,304,189 issued May 28, 2019, which is acontinuation of U.S. application Ser. No. 14/608,471 filed Jan. 29,2015, now U.S. Pat. No. 9,530,207 issued Dec. 27, 2016, which is acontinuation of U.S. application Ser. No. 13/006,206 filed Jan. 13,2011, now U.S. Pat. No. 8,965,076 issued Feb. 24, 2015, which claims thebenefit of U.S. Provisional Application No. 61/294,811 filed on Jan. 13,2010 and U.S. Provisional Application No. 61/321,029 filed on Apr. 5,2010, each of which is hereby incorporated by reference in its entirety.

BACKGROUND Field of the Invention

Embodiments disclosed herein relate to methods and systems for analysisof image data generated at multiple reference points, and particularlyto image and sequence data generated during DNA sequencing.

Description of the Related Art

The analysis of image data presents a number of challenges, especiallywith respect to comparing images of an item or structure that arecaptured from different points of reference. One field that exemplifiesmany of these challenges is that of nucleic acid sequence analysis.

The detection of specific nucleic acid sequences present in a biologicalsample has a wide variety of applications, such as identifying andclassifying microorganisms, diagnosing infectious diseases, detectingand characterizing genetic abnormalities, identifying genetic changesassociated with cancer, studying genetic susceptibility to disease, andmeasuring response to various types of treatment. A valuable techniquefor detecting specific nucleic acid sequences in a biological sample isnucleic acid sequencing.

Nucleic acid sequencing methodology has evolved significantly from thechemical degradation methods used by Maxam and Gilbert and the strandelongation methods used by Sanger. Today, there are a number ofdifferent processes being employed to elucidate nucleic acid sequence. Aparticularly popular sequencing process is sequencing-by-synthesis. Onereason for its popularity is that this technique can be easily appliedto massively parallel sequencing projects. For example, using anautomated platform, it is possible to carry out hundreds of thousands ofsequencing reactions simultaneously. Sequencing-by-synthesis differsfrom the classic dideoxy sequencing approach in that, instead ofgenerating a large number of sequences and then characterizing them at alater step, real time monitoring of the incorporation of each base intoa growing chain is employed. Although this approach might be viewed asslow in the context of an individual sequencing reaction, it can be usedfor generating large amounts of sequence information in each sequencingcycle when hundreds of thousands to millions of reactions are performedin parallel. Despite these advantages, the vast size and quantity ofsequence information obtained through such methods can limit the speedand quality of analysis of sequence data. Thus, there is a need formethods and systems which improve the speed and accuracy of analysis ofnucleic acid sequencing data.

SUMMARY

The present technology relates to methods and systems for analysis ofimage data. In particular exemplary embodiments, the technology relatesto methods and systems for analysis of image data generated duringnucleic acid sequencing. In some embodiments, such methods and systemsinclude data acquisition and/or storage functions. In some embodimentsof the present invention, such methods and systems permit the analysisof image data from sequencing processes with improved speed andaccuracy.

In some embodiments of the technology described herein, methods ofperforming image analysis are provided that allows image analysis tooccur while storing large amounts of image data. The methods can includeperforming image analysis in the background of a process thatpreferentially acquires image data. Such methods can be performed by asingle processor capable of time-division multiplexing or othermultithreading process. In other embodiments, such methods areimplemented using multiple processes that may or may not overlaptemporally, for example, by utilizing two or more separate processors.An advantage that may be realized by such methods is a reduction in datastorage requirements since analyzed data typically requires less storagespace than the image data from which the analyzed data is derived.

In certain aspects, the methods described herein can include the stepsof providing a first data set to store on a storage device; providing asecond data set for analysis; processing the first data set and thesecond data set; wherein the processing comprises acquiring and storingthe first data set on the storage device and analyzing the second dataset when the processor is not acquiring the first data set. In certainaspects, the processing step includes identifying at least one instanceof a conflict between acquiring the first data set and analyzing thesecond data set; and resolving the conflict in favor of acquiring imagedata such that acquiring the first data set is given priority. In someaspects, the first data set and the second data set are the same dataset. In other aspects, the first data set and the second data set arenot the same. In certain aspects, preliminary processing can beperformed and follow-on processing such as base-calling and qualityscoring can be performed separately.

In certain aspects of the methods described herein, the first data setcomprises image files. In certain aspects, the second data set comprisesimage files. In certain aspects, the second data set comprises dataobtained from an analysis of image files. In certain aspects, analyzingthe second data set comprises advancing a file through a series ofanalyses, wherein each subsequent analysis in the series advances a filefrom a more preliminary state to a more advanced state. In certainaspects, advancing includes identifying at least one instance of aconflict between analyzing one file in an advanced state and analyzing afile that is in a more preliminary state; and resolving the conflict infavor of analyzing a file that is in a more preliminary state. In someaspects, the data obtained comprises one or more of the featuresselected from the group consisting of: the location of a spot within animage file; the intensity of a spot in an image file; the value of noiseassociated with an image file; the identity of a spot as representing achemical feature; and a quality score associated with a spot in an imagefile.

In particular embodiments, a method of performing image analysis in realtime is provided. The method can include steps of (a) providing aspecimen having multiple features; (b) providing a detector directed tothe specimen; (c) subjecting the specimen to multiple cycles oftreatment and image acquisition, wherein the treatment changes acharacteristic of at least a subset of the features in the specimen andthe detector acquires images that distinguishes the changes of thecharacteristic; and (d) transferring the images from the detector to aprocessor, wherein the processor (i) transfers the images to least onestorage capacity, (ii) analyzes the images to register multiple imagesto a template and identify a sequence of changes in the characteristicsfor the features in the specimen, thereby producing analyzed data, and(iii) transfers the analyzed data to the at least one storage capacity,wherein the processor identifies at least one instance of a conflictbetween transferring a first image and analyzing a second image; and theprocessor resolves the conflict in favor of transferring the first imageprior to analyzing the second image.

In some aspects, the specimen can include a nucleic acid array and thefeatures can include nucleic acids having different nucleotidesequences. The treatment that occurs in multiple cycles of the methodcan include adding reagents for a nucleic acid sequencing technique.Embodiments carried out for a nucleic acid array can include a processorthat further determines at least a portion of the nucleotide sequencepresent at individual features of the nucleic acid array.

The detector that is used in a method of performing image analysis inreal time can include a camera. The camera can acquire images thatdistinguish changes in the color of at least a subset of the features ina specimen. For example, color changes resulting from cycles of asequencing technique can be distinguished using a camera.

A processor used in a method of performing image analysis in real timecan also be used to identify at least one instance of a conflict betweenregistering the multiple images and identifying the sequence of changes,and the processor can resolve the conflict in favor of registering themultiple images prior to identifying the sequence of changes.

In some aspects of a method of performing image analysis in real timethe processor can discard a first image after the analyzing of the imagedata and after the transferring of the analyzed data. The processor canfurther discard the first image before completing the multiple cycles oftreatment and image acquisition to generate at least a second image,wherein the processor can register the first image and the second imageto a template and identify a sequence of changes in the characteristicsfor features in the first image and in the second image.

Also provided herein are systems for performing image analysis. Thesystems can include a processor; a storage capacity; and a program forimage analysis, the program comprising instructions for processing afirst data set for storage and the second data set for analysis, whereinthe processing comprises acquiring and storing the first data set on thestorage device and analyzing the second data set when the processor isnot acquiring and/or storing the first data set. In certain aspects, theprogram includes instructions for identifying at least one instance of aconflict between acquiring and/or storing the first data set andanalyzing the second data set; and resolving the conflict in favor ofacquiring and/or storing image data such that acquiring and/or storingthe first data set is given priority. In certain aspects, the first dataset comprises image files obtained from an optical imaging device, suchas a charge coupled device (CCD) camera or a complementary metal-oxidesemiconductor (CMOS) camera. In certain aspects, the optical imagingdevice can be directly integrated with the system. In other embodiments,the optical imaging device can be removably coupled to the system. Insome aspects, the optical imaging device comprises a light source and adetection device.

A particularly useful imaging system can include (a) a detector; (b) atleast one storage capacity; and (c) a processor configured for (i)transferring image data from the detector to the to least one storagecapacity, (ii) analyzing the image data to produce analyzed data,wherein the analyzing of the image data set includes registeringmultiple image data sets to a template and identifying a differentcharacteristic of a feature between two or more images, and (iii)transferring the analyzed data to the at least one storage capacity,wherein the processor comprises instructions for identifying at leastone instance of a conflict between transferring a first image data setand analyzing a second image data set; and resolving the conflict infavor of transferring image data such that the transferring of the firstimage data set is given priority over the analyzing of the second imagedata set. The processor can further include instructions for identifyingat least one instance of a conflict between the registering of themultiple image data sets and the identifying of the differentcharacteristic of the feature between two or more images; and forresolving the conflict in favor of registering such that the registeringis given priority over the identifying. The different characteristic ofa feature between two or more images that is identified can be a changein color.

The processor that forms part of an imaging system can further includeinstructions for analyzing a sequence of changes for the differentcharacteristic of the feature between the two or more images. Moreover,the processor can further include instructions for determining anucleotide sequence at the individual feature from the sequence ofchanges.

In particular embodiments, the processor is further configured fordiscarding the image data after the analyzing of the image data andafter the transferring of the analyzed data. If desired, the processorcan be further configured for discarding the image data beforetransferring a second set of image data from the detector to the atleast one storage capacity.

Also provided herein are methods of tracking the location of features ofa specimen across a set of images of the specimen captured at differentreference points. The methods can comprise the steps of: (a) selecting asubset of images, wherein the images of the subset depict signalscorresponding to features of the specimen, and wherein the images of thesubset are captured at different reference points; (b) selecting aprimary image from the subset of images; (c) registering the signalsdepicted in the images of the subset of images with the signals depictedin the primary image so as to determine the location of the signalsdepicted in the images with respect to each other, thereby producingsignal clumps; (d) selecting a signal from each of the signal clumps,thereby forming a template that permits the identification of thelocations of features of the specimen; and (e) registering remainingimages in the set of images with the template. In some aspects, theprimary image comprises either a single image or a compilation ofmultiple merged images.

In certain aspects, the specimen can comprise at least one tilecomprising an array of molecules. In certain aspects, the specimencomprises a plurality of tiles. In certain aspects, the array ofmolecules comprises a plurality of features. In certain aspects, afeature of the plurality of features comprises multiple copies of amolecule of the array of molecules. In certain aspects, the moleculecomprises a nucleic acid. Multiple copies of nucleic acids at a featurecan be sequenced, for example, by providing a labeled nucleotide base tothe array of molecules, thereby extending a primer hybridized to anucleic acid within a feature so as to produce a signal corresponding toa feature comprising the nucleic acid. In preferred embodiments, thenucleic acids within a feature are identical or substantially identicalto each other.

In some of the image analysis methods described herein, each image inthe set of images includes colors signals, wherein a different colorcorresponds to a different nucleotide base. In some aspects, each imageof the set of images comprises signals having a single color selectedfrom at least four different colors. In certain aspects, each image inthe set of images comprises signals having a single color selected fromfour different colors.

With respect to certain methods described herein, nucleic acids can besequenced by providing, four different labeled nucleotide bases to thearray of molecules so as to produce four different images, each imagecomprising signals having a single color, wherein the signal color isdifferent for each of the four different images, thereby producing acycle of four color images that corresponds to the four possiblenucleotides present at a particular position in the nucleic acid. Incertain aspects, such methods can further comprise providing additionallabeled nucleotide bases to the array of molecules, thereby producing aplurality of cycles of color images.

In some aspects of the image analysis methods described herein,selecting a primary image further comprises selecting the four differentcolor images from a single cycle within the subset of images; mergingsignals from at least two different color images of each cycle into acandidate primary image for the cycle; repeating the selecting andmerging steps, thereby forming a plurality of candidate primary images;and selecting the candidate primary image from among the candidateprimary images that includes the most signals, thereby obtaining aprimary image.

In some aspects, the image analysis methods described herein furthercomprise the step of selecting a secondary image, wherein the secondaryimage is the candidate primary image that includes the second mostsignals. In certain aspects, the secondary image comprises either asingle image or a compilation of multiple merged images.

In certain aspects, the method further comprises registering the signalsfrom the secondary image with the signals from the primary image. Incertain aspects, the method further comprises registering signals fromnonselected images with the signals from the secondary image, whereinthe nonselected images are obtained from the image cycle from which theprimary image was obtained. In certain aspects, the method furthercomprises registering signals from remaining images in the subset withsignals from the primary image.

In some aspects of the image analysis methods described herein, thesignals from at least two different color images are subject tocross-talk, wherein cross-talk between two signal channels allowssignals from one color image to appear in the other color image. Incertain aspects, the signals from at least two different color imagescomprise signals corresponding to A and C.

In some aspects of the image analysis methods described herein, thesignals from at least two different color images are not subject tocross-talk or exhibit only insubstantial or insignificant cross-talk. Incertain aspects, the signals from at least two different color imagescomprise signals corresponding to A and G or A and T. In other aspects,the signals from at least two different color images comprise signalscorresponding to T and C, T and A, G and C or G and A. In such methods,where channels that do not exhibit appreciable cross-talk are used forregistration, image alignment or registration can be performed bydetermining the non-alignment between non-cross-talking channels in thesame cycle. Accordingly, in some embodiments the step of registering thesignals depicted in the images of the subset of images can includedetermining a minimum, near minimum or relatively low cross-correlationbetween channels which are not expected to exhibit appreciablecross-talk.

In some embodiments of the image analysis methods described herein,image registration can be performed over a wide range of densities. Insome embodiments, image registration can occur over a range from about10 clusters/mm², to about 1,000,000 clusters/mm². In other embodiments,registration can occur over a range from about 100,000 clusters/mm² toabout 1,000,000 clusters/mm².

In some aspects, the image analysis methods described herein areutilized to generate a template from a subset of images, wherein thetemplate permits the identification of the location of a feature of aspecimen. In certain aspects, the template that permits theidentification of the locations of features of the specimen is saved toa locations file.

Also provided herein are systems for tracking the location of featuresof a specimen across a set of images of the specimen captured atdifferent reference points. The systems can comprise: a processor; astorage capacity; and a program for tracking the location of featuresacross different images, the program comprising instructions for:selecting a subset of images, wherein the images of the subset depictsignals corresponding to features of the specimen, and wherein theimages of the subset are captured at different reference points;selecting a primary image from the subset of images; registering thesignals depicted in the images of the subset of images with the signalsdepicted in the primary image so as to determine the location of thesignals depicted in the images with respect to each other, therebyproducing signal clumps; selecting a signal from each of the signalclumps, thereby forming a template that permits the identification ofthe locations of features of the specimen; and registering remainingimages in the set of images with the template. In certain aspects, theprimary image comprises either a single image or a compilation ofmultiple merged images.

Also provided herein are systems for tracking the location of featuresof an array across a set of images of the array captured at differenttime points. The systems can comprise: a processor; a storage capacity;and a program for tracking the location of features across differentimages, the program comprising instructions for: selecting a subset ofimages, wherein the images of the subset depict signals corresponding tofeatures of the array, and wherein the images of the subset are capturedat different time points; selecting a primary image from the subset ofimages; and registering the signals depicted in the images of the subsetof images with the signals depicted in the primary image, so as todetermine the location of the signals depicted in the images withrespect to each other, thereby producing signal clumps; selecting asignal from each of the signal clumps, thereby forming a template thatpermits the identification of the locations of features of the array;and registering remaining images in the set of images with the template.

In some aspects of the above embodiments, the system can comprise a flowcell. In some aspects, the flow cell comprises lanes, or otherconfigurations, of tiles, wherein at least some of the tiles compriseone or more arrays of features. In some aspects, the features comprise aplurality of molecules such as nucleic acids. In certain aspects, theflow cell is configured to deliver a labeled nucleotide base to an arrayof nucleic acids, thereby extending a primer hybridized to a nucleicacid within a feature so as to produce a signal corresponding to afeature comprising the nucleic acid. In preferred embodiments, thenucleic acids within a feature are identical or substantially identicalto each other.

In some of the systems for image analysis described herein, each imagein the set of images includes color signals, wherein a different colorcorresponds to a different nucleotide base. In some aspects, each imageof the set of images comprises signals having a single color selectedfrom at least four different colors. In some aspects, each image in theset of images comprises signals having a single color selected from fourdifferent colors. In some of the systems described herein, nucleic acidscan be sequenced by providing four different labeled nucleotide bases tothe array of molecules so as to produce four different images, eachimage comprising signals having a single color, wherein the signal coloris different for each of the four different images, thereby producing acycle of four color images that corresponds to the four possiblenucleotides present at a particular position in the nucleic acid. Incertain aspects, the system comprises a flow cell that is configured todeliver additional labeled nucleotide bases to the array of molecules,thereby producing a plurality of cycles of color images.

In certain aspects of the systems for image analysis described herein,the program for tracking the location of features across differentimages further comprises instructions for: selecting the four differentcolor images from a single cycle within the subset of images; mergingsignals from at least two different color images of each cycle into acandidate primary image for the cycle; repeating the selecting andmerging steps, thereby forming a plurality of candidate primary images;and selecting the candidate primary image from among the candidateprimary images that includes the most signals, thereby obtaining aprimary image.

In certain aspects, the program for tracking the location of featuresacross different images further comprises instructions for selecting asecondary image, wherein the secondary image is the candidate primaryimage that includes the second most signals. In certain aspects, thesecondary image comprises either a single image or a compilation ofmultiple merged images.

In certain aspects, the program for tracking the location of featuresacross different images further comprises instructions for registeringthe signals from the secondary image with the signals from the primaryimage. In certain aspects, the program for tracking the location offeatures across different images further comprises instructions forregistering signals from nonselected images with the signals from thesecondary image, wherein the nonselected images are obtained from theimage cycle from which the primary image was obtained. In certainaspects, the program for tracking the location of features acrossdifferent images further comprises instructions for registering signalsfrom remaining images in the subset with signals from the primary image.

In some aspects of the systems for image analysis described herein, thesignals from at least two different color images are subject tocross-talk, wherein cross-talk between two signal channels allowssignals from one color image to appear in the other color image. Incertain aspects, the signals from at least two different color imagescomprise signals corresponding to A and C.

In some aspects of the systems for image analysis described herein, thesignals from at least two different color images are not subject tocross-talk or exhibit only insubstantial or insignificant cross-talk. Incertain aspects, the signals from at least two different color imagescomprise signals corresponding to A and G or A and T. In other aspects,the signals from at least two different color images comprise signalscorresponding to T and C, T and A, G and C or G and A. In such systems,where channels that do not exhibit appreciable cross-talk are used forregistration, image alignment or registration can be performed bydetermining the non-alignment between non-cross-talking channels in thesame cycle. Accordingly, in some embodiments the instructions forregistering the signals depicted in the images of the subset of imagescan include determining a minimum, near minimum or relatively lowcross-correlation between channels which are not expected to exhibitappreciable cross-talk.

In some aspects, the systems for image analysis described herein areutilized to generate a template from a subset of images, wherein thetemplate permits the identification of the location of a feature of aspecimen. In certain aspects, the template that permits theidentification of the locations of features of the specimen is saved toa locations file.

In some embodiments of the systems for image analysis described herein,image registration can be performed over a wide range of densities. Insome embodiments, image registration can occur over a range from about10 clusters/mm², to about 1,000,000 clusters/mm². In other embodiments,registration can occur over a range from about 100,000 clusters/mm² toabout 1,000,000 clusters/mm².

Also provided herein are methods of generating a template for imageregistration. The methods can comprise the steps of: (a) selecting asubset from a set of images of a specimen, the subset of imagescomprising a plurality of image cycles, wherein the images of the subsetdepict signals corresponding to features of the specimen, and whereinthe images of the subset are captured at different reference points; (b)selecting a primary image from the subset of images, wherein the primaryimage is the image having the most signals from the subset of images;(c) selecting a secondary image from the subset of images, wherein thesecondary images is the image having the second most signals from thesubset of images; (d) registering the signals from the secondary imagewith the signals from the primary image; (e) registering signals fromnonselected images with the signals from the secondary image, whereinthe nonselected images are obtained from the image cycle from which theprimary image was obtained; (f) registering signals from remainingimages in the subset with signals from the primary image so as todetermine the location of the signals depicted in the images withrespect to each other, thereby producing signal clumps; and (g)selecting a signal from each of the signal clumps, thereby forming atemplate that permits the identification of the locations of features ofthe specimen.

In certain aspects of the methods described herein, the primary imagecomprises either a single image or a compilation of multiple mergedimages. In some aspects, the secondary image comprises either a singleimage or a compilation of multiple merged images. In some aspects, theprimary and secondary images are used to generate a template thatpermits the identification of the location of a feature of the specimen.In certain aspects, the template that permits the identification of thelocations of features of the specimen is saved to a locations file.

Also provided herein are systems of generating a template for imageregistration. The systems can comprise: a processor; a storage capacity;and a program for generating a template for image registration, theprogram comprising instructions for: (a) selecting a subset from a setof images of a specimen, the subset of images comprising a plurality ofimage cycles, wherein the images of the subset depict signalscorresponding to features of the specimen, and wherein the images of thesubset are captured at different reference points; (b) selecting aprimary image from the subset of images, wherein the primary image isthe image having the most signals from the subset of images; (c)selecting a secondary image from the subset of images, wherein thesecondary images is the image having the second most signals from thesubset of images; (d) registering the signals from the secondary imagewith the signals from the primary image; (e) registering signals fromnonselected images with the signals from the secondary image, whereinthe nonselected images are obtained from the image cycle from which theprimary image was obtained; (f) registering signals from remainingimages in the subset with signals from the primary image so as todetermine the location of the signals depicted in the images withrespect to each other, thereby producing signal clumps; and (g)selecting a signal from each of the signal clumps, thereby forming atemplate that permits the identification of the locations of features ofthe specimen.

In certain aspects of the systems described herein, the primary imagecomprises either a single image or a compilation of multiple mergedimages. In some aspects, the secondary image comprises either a singleimage or a compilation of multiple merged images. In some aspects, theprimary and secondary images are used to generate a template thatpermits the identification of the location of a feature of the specimen.In certain aspects, the template that permits the identification of thelocations of features of the specimen is saved to a locations file.

Also provided herein is a method of selecting a signal from a cluster ofsignals in an image. The method can comprise the steps of: discardingany signals that do not appear within a cluster of signals, the clusterof signals having a defined cluster radius; and selecting the signalwith the highest intensity of signals within a cluster. In certainaspects, the selecting step further comprises discarding any signalwhich already has a signal neighbor within the cluster radius.

In certain aspects, the selecting step further comprises ordering thesignals. In certain aspects, ordering the signals comprises ordering thesignals by detection count. In some embodiments, the detection count isequivalent to the number of times a signal is detected within a selectedradius. In some embodiments, the selected radius is measured from apre-selected signal. In some embodiments, the pre-selected signal can bethe first signal detected within a group of signals. In otherembodiments, the pre-selected signal can be the brightest signal in apredicted feature location. In still other embodiments, the selectedradius is established without reference to a pre-selected signal. Inpreferred embodiments, the selected radius is about 0.5 pixels; however,it will be appreciated that the selected radius may be larger or smallerdepending on a variety of factors which include, but are not limited to,the quality of data acquisition and/or the end user's needs.

In certain aspects, ordering the signals comprises ordering the signalsby intensity or brightness. In certain aspects, the intensity orbrightness is selected from the group consisting of brightness relativeto neighboring pixels, brightness relative to a noise estimate, absolutebrightness, brightness as a percentile, and extracted (bilinear)brightness. In preferred aspects, the brightness is brightness relativeto neighboring pixels. In some such aspects, the neighboring pixels maybe present within a defined radius. In some preferred aspects, thedefined radius is lowered in response to increased signal density. Insuch preferred aspects, the defined radius can be, for example, 2.0pixels, 1.5 pixels 1.0 pixel or less than 1.0 pixel. In certain aspects,ordering the signals comprises ordering the signals by detection countthen ordering the signals by intensity.

In other embodiments of the signal selection methods described herein,additional or alternate factors influencing the selection are employed.In some such embodiments, signal selection methods include a chastitydetermination. In exemplary embodiments, the chastity of a spot, forexample, the intensity of a cluster in different color channels, iscalculated and used in subsequent comparison or determination steps. Incertain embodiments, the procedure for including a chastitydetermination in spot selection can comprise (a) generating a list ofidentified spots and intensities for each template cycle; (b) providinga preliminary base call for each spot; (c) determining the chastity ofeach base call; (d) identifying spots with two or more chastityfailures; (e) eliminating spots having chastity values lower than athreshold value; (f) determining the sample diversity and/or theprobability that clusters will match base calls by chance; and (g)analyzing spots in iteration from highest to lowest chastity overtemplate cycles, wherein the analysis can include one or more steps of:(i) discarding the spot being analyzed if another spot has beenpreviously assigned within a first radius; and (ii) discarding the spotbeing analyzed if another spot has been previously assigned within asecond radius and the base calls for the spot meet a threshold chastityvalue.

It will be appreciated that steps in the spot selection methodsdescribed in the previous paragraph need not include every step setforth therein. While spot selection methods that utilize chastity mayinclude a chastity determination step, other steps in spot selection maybe eliminated or substituted.

In some embodiments of the above-described method, the first and secondradii have different values. In other embodiments, the first and secondradii have the same values.

It will also be appreciated that the threshold value of chastity can bedetermined by a skilled artisan in view of the required application. Insome embodiments, the threshold chastity values range from about 0.5 toabout 0.99.

Also provided herein is a system for selecting a signal from a clusterof signals in an image. The system can comprise: a processor; a storagecapacity; and a program for selecting a signal from a cluster ofsignals, the program comprising instructions for: discarding any signalsthat do not appear within a cluster of signals, the cluster of signalshaving a defined cluster radius; and selecting the signal with thehighest intensity of signals within a cluster. In certain aspects,selecting further comprises discarding any signal which already has asignal neighbor within the cluster radius.

In certain aspects, the selecting step further comprises ordering thesignals. In certain aspects, ordering the signals comprises ordering thesignals by detection count. In some embodiments, the detection count isequivalent to the number of times a signal is detected within a selectedradius. In some embodiments, the selected radius is measured from apre-selected signal. In some embodiments, the pre-selected signal can bethe first signal detected within a group of signals. In otherembodiments, the pre-selected signal can be the brightest signal in apredicted feature location. In still other embodiments, the selectedradius is established without reference to a pre-selected signal. Inpreferred embodiments, the selected radius is about 0.5 pixels; however,it will be appreciated that the selected radius may be larger or smallerdepending on a variety of factors which include, but are not limited to,the quality of data acquisition and/or the end user's needs.

In certain aspects, ordering the signals comprises ordering the signalsby intensity or brightness. In certain aspects, the intensity orbrightness is selected from the group consisting of brightness relativeto neighboring pixels, brightness relative to a noise estimate, absolutebrightness, brightness as a percentile, and extracted (bilinear)brightness. In preferred aspects, the brightness is brightness relativeto neighboring pixels. In some such aspects, the neighboring pixels maybe present within a defined radius. In some preferred aspects, thedefined radius is lowered in response to increased signal density. Insuch preferred aspects, the defined radius can be, for example, 2.0pixels, 1.5 pixels, 1.0 pixel or less than 1.0 pixel. In certainaspects, ordering the signals comprises ordering the signals bydetection count then ordering the signals by intensity.

In additional embodiments of systems for selecting a signal describedherein, additional or alternate factors influencing the selection areemployed. In some such embodiments, such systems include instructionsfor determining chastity. In exemplary embodiments, the chastity of aspot, for example, the intensity of a cluster in different colorchannels, is calculated and used in subsequent comparison ordetermination steps. In certain embodiments, the instructions forincluding a chastity determination in spot selection can comprise (a)generating a list of identified spots and intensities for each templatecycle; (b) providing a preliminary base call for each spot; (c)determining the chastity of each base call; (d) identifying spots withtwo or more chastity failures; (e) eliminating spots having chastityvalues lower than a threshold value; (f) determining the samplediversity and/or the probability that clusters will match base calls bychance; and (g) analyzing spots in iteration from highest to lowestchastity over template cycles, wherein the analysis can include one ormore steps of: (i) discarding the spot being analyzed if another spothas been previously assigned within a first radius; and (ii) discardingthe spot being analyzed if another spot has been previously assignedwithin a second radius and the base calls for the spot meet a thresholdchastity value.

It will be appreciated that steps described in the previous paragraphneed not include every step set forth therein. While systems forperforming spot selection that utilize chastity may include instructionsfor chastity determination, other steps in spot selection may beeliminated or substituted.

In some embodiments of the above-described system for spot selection,the first and second radii have different values. In other embodiments,the first and second radii have the same values.

It will also be appreciated that the threshold value of chastity can bedetermined by skilled artisan in view of the required application. Insome embodiments, the threshold chastity values range from about 0.5 toabout 0.99.

In addition to the foregoing aspects, also provided herein are methodsof assigning colors to features in an image. In some embodiments, suchmethods can comprise the steps of: determining a preliminary colormatrix for a feature; evaluating quality of the preliminary colorassignment; refining the preliminary color matrix based on the colorassignment, thereby forming a refined color matrix; and making a finalcolor assignment for a feature based on the refined color matrix. Incertain aspects, the methods can further comprise the step of making apreliminary color assignment for a feature based on the preliminarycolor matrix, wherein the preliminary color assignment is made prior tothe evaluating step.

In certain aspects described herein, the refined color matrix can beused to correct for cross-talk between channels. In some aspects, thestep of refining the preliminary color matrix comprisesre-orthogonalizing signal intensities for the preliminary color matrixat one or more features. In certain aspects, the refining step comprisesre-normalizing signal intensities for the preliminary color matrix atone or more features. In certain preferred aspects, the refining stepcomprises both re-orthogonalizing and re-normalizing signal intensitiesfor the preliminary color matrix at one or more features.

Also provided herein is a method of identifying a nucleotide base in anucleic acid sequence. The method can comprise: determining theintensity of each differently colored signal present at a particularfeature on an array at a particular time, wherein each differentlycolored signal corresponds to a different nucleotide base; repeating thedetermining step for a plurality of features on an array at theparticular time; refining the signal intensity for the plurality offeatures then repeating the determining step for the plurality offeatures; and selecting for one or more features of the plurality offeatures the refined signal having the highest intensity, wherein thesignal having the highest intensity at a particular feature correspondsto the identity of the nucleotide base present at the particularfeature.

In some aspects of the methods for identifying a nucleotide basedescribed herein, the refining step comprises re-orthogonalizing thesignal intensities for the plurality of features. In certain aspects,the refining step comprises re-normalizing the signal intensities forthe plurality of features. In certain preferred aspects, the refiningstep comprises both re-orthogonalizing and re-normalizing the signalintensities for the plurality of features.

Additional methods for assigning colors to features in an image involveforming an alternate color matrix to correct for cross-talk betweenchannels. In some embodiments, the steps of such methods comprise (a)obtaining intensity measurements from two channels that exhibitcross-talk; (b) determining the distribution of the intensitymeasurements; (c) identifying local maxima in the distribution, therebygenerating a cross-talk coefficient; (d) and normalizing the twochannels using the cross-talk coefficient. In some embodiments the twochannels are A and C. In other embodiments, the two channels are G andT.

In preferred embodiments, determining the distribution of the intensitymeasurements comprises generating a histogram of intensity data. In somesuch embodiments, the intensity measurements from the two channels arefirst converted to polar coordinates. In preferred embodiments, ahistogram generated from such intensity data comprises a radius-weightedhistogram.

In additional embodiments of the methods for assigning colors to animage, normalization further comprises a base calling step, a step ofdetermining the chastity associated with the base call or both.

Also provided herein is a system for refining signals within an image,the system comprising: a processor; a storage capacity; and a programfor assigning colors to features in an image, the program comprisinginstructions for: determining a preliminary color matrix for a feature;evaluating quality of the preliminary color assignment; refining thepreliminary color matrix based on the color assignment, thereby forminga refined color matrix; and making a final color assignment for afeature based on the refined color matrix.

In certain aspects described herein, the program further comprisesinstructions for making a preliminary color assignment for a featurebased on the preliminary color matrix. In certain aspects, the refinedcolor matrix can be used to correct for cross-talk between channels. Insome aspects, the instructions for refining the preliminary color matrixcomprise re-orthogonalizing signal intensities for the preliminary colormatrix at one or more features. In certain aspects, the instructions forrefining the preliminary color matrix comprise re-normalizing signalintensities for the preliminary color matrix at one or more features. Incertain aspects, the instructions for refining the preliminary colormatrix comprise both re-orthogonalizing and re-normalizing signalintensities for the preliminary color matrix at one or more features.

Also provided herein is a system for identifying a nucleotide base in anucleic acid sequence, the system comprising: a processor; a storagecapacity; and a program for identifying a nucleotide base in a nucleicacid sequence, the program comprising instructions for: determining theintensity of each differently colored signal present at a particularfeature on an array at a particular time, wherein each differentlycolored signal corresponds to a different nucleotide base; repeating thedetermining step for a plurality of features on an array at theparticular time; refining the signal intensity for the plurality offeatures then repeating the determining step for the plurality offeatures; and selecting for one or more features of the plurality offeatures the refined signal having the highest intensity, wherein thesignal having the highest intensity at a particular feature correspondsto the identity of the nucleotide base present at the particularfeature.

In some aspects of the systems for identifying a nucleotide basedescribed herein, the instructions for refining comprisere-orthogonalizing the signal intensities for the plurality of features.In certain aspects, the instructions for refining comprisere-normalizing the signal intensities for the plurality of features. Incertain preferred aspects, the instructions for refining comprise bothre-orthogonalizing and re-normalizing the signal intensities for theplurality of features. In certain aspects, the system further comprisesa flow cell.

Additionally, provided herein is a system for refining signals within animage, the system comprising: a processor; a storage capacity; and aprogram for assigning colors to features in an image, the programcomprising instructions for forming an alternate color matrix to correctfor cross-talk between channels. In some embodiments, systems compriseinstructions for (a) obtaining intensity measurements from two channelsthat exhibit cross-talk; (b) determining the distribution of theintensity measurements; (c) identifying local maxima in thedistribution, thereby generating a cross-talk coefficient; (d) andnormalizing the two channels using the cross-talk coefficient. In someembodiments the two channels are A and C. In other embodiments, the twochannels are G and T.

In preferred embodiments, determining the distribution of the intensitymeasurements comprises generating a histogram of intensity data. In somesuch embodiments, the intensity measurements from the two channels arefirst converted to polar coordinates. In preferred embodiments, ahistogram generated from such intensity data comprises a radius-weightedhistogram.

In additional embodiments of the systems for assigning colors to animage, normalization further comprises a base calling step, a step ofdetermining the chastity associated with the base call or both.

Also provided herein are methods of evaluating the quality of a basecall from a sequencing read. In some embodiments, the methods cancomprise the steps of: calculating a set of predictor values for thebase call; and then using the predictor values to look up a qualityscore in a quality table. In certain aspects, the quality table isgenerated using Phred scoring on a calibration data set, the calibrationset being representative of run and sequence variability. In certainaspects, the predictor values are selected from the group consisting of:approximate homopolymer; intensity decay; penultimate chastity; signaloverlap with background (SOWB); and shifted purity G adjustment.

In certain aspects, the method further comprises the steps of:discounting unreliable quality scores at the end of each read;identifying reads where the second worst chastity in the first 25 basecalls is below a pre-established threshold; and marking the reads aspoor quality data. In certain aspects, the method further comprisesusing an algorithm to identify a threshold of reliability. In certainaspects, reliable base calls comprise q-values, or other valuesindicative of data quality or statistical significance, above thethreshold and unreliable base calls comprise q-values, or other valuesindicative of data quality or statistical significance, below thethreshold. In certain aspects, the algorithm comprises an End AnchoredMaximal Scoring Segments (EAMSS) algorithm. In certain aspects, thealgorithm uses a Hidden Markov Model that identifies shifts in the localdistributions of quality scores.

Also provided herein is a system for evaluating the quality of a basecall from a sequencing read, the system comprising: a processor; astorage capacity; and a program for evaluating the quality of a basecall from a sequencing read, the program comprising instructions for:calculating a set of predictor values for the base call; and then usingthe predictor values to look up a quality score in a quality table. Incertain aspects, the quality table is generated using Phred scoring on acalibration data set, the calibration set being representative of runand sequence variability. In certain aspects, the predictor values areselected from the group consisting of: approximate homopolymer;intensity decay; penultimate chastity; signal overlap with background(SOWB); and shifted purity G adjustment.

In certain aspects, the system can further comprise instructions for:discounting unreliable quality scores at the end of each read;identifying reads where the second worst chastity in the first 25 basecalls is below a pre-established threshold; and marking the reads aspoor quality data. In certain aspects, the system further comprisesinstructions for using an algorithm to identify a threshold ofreliability. In certain aspects, the reliable base calls compriseq-values, or other values indicative of data quality or statisticalsignificance, above the threshold and unreliable base calls compriseq-values, or other values indicative of data quality or statisticalsignificance, below the threshold. In certain aspects, the algorithmcomprises an End Anchored Maximal Scoring Segments (EAMSS) algorithm. Incertain aspects, the algorithm uses a Hidden Markov Model thatidentifies shifts in the local distributions of quality scores.

Also provided herein are methods of verifying that sequence dataobtained from a specimen comprising a plurality of arrays isnon-artifactual. In some embodiments, such methods, compriseincorporating a control nucleic acid into one or more arrays of theplurality of arrays and verifying that the control nucleic acid has beenproperly sequenced. In certain aspects, the control nucleic acid has aknown sequence. In some such aspects, the control nucleic acid can berandomly distributed on the specimen so as to form all or part of one ormore arrays in the plurality of arrays. In other such aspects, thecontrol nucleic acid can be arbitrarily distributed on the specimen soas to form all or part of one or more arrays in the plurality of arrays.In certain aspects, the control nucleic acid is derived from an organismwith a substantially stable and/or substantially non-variable genome. Incertain aspects, the control nucleic acid is obtained from, or otherwisederived from, a bacteriophage genome. In certain aspects, thebacteriophage genome is the Phi X 174 genome.

In certain aspects, the plurality of arrays are present in a flow cellhaving a plurality of channels. In certain aspects, each channel of theplurality of channels comprises a plurality of tiles. In some aspects, atile comprises a complete array representing all or part of the genomeof a single species or a single individual of a species. In otheraspects, a tile comprises one or more nucleic acids representative of anorganism or class or organisms. In certain aspects, the control nucleicacid is provided in multiple different tiles within a channel of theflow cell. In a preferred embodiment, a tile comprising the controlnucleic acid includes only nucleic acids having a nucleotide sequenceidentical to or substantially identical to the control nucleotidesequence. In certain aspects, the control nucleic acid is provided indifferent channels of the flow cell.

Additional embodiments can be found in U.S. Provisional PatentApplication No. 61/294,811 filed on Jan. 13, 2010 and Provisional PatentApplication No. 61/321,029 filed on Apr. 5, 2010, the contents of whichare incorporated herein by reference in their entireties.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are a schematic drawing demonstrating the generalworkflow in real time image analysis.

FIG. 2A is a schematic demonstrating a method of template generation.

FIG. 2B is a schematic demonstrating a particularly robust method oftemplate generation.

FIGS. 3A and 3B are diagrams showing some advantages of certainembodiments of template generation methods described herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The analysis of image data presents a number of challenges, especiallywith respect to comparing images of an item or structure that arecaptured from different points of reference. Most image analysismethodology employs, at least in part, steps for aligning multipleseparate images with respect to each other based on characteristics orelements present in both images. Various embodiments of the compositionsand methods disclosed herein improve upon previous methods for imageanalysis.

Recently, tools have been developed that acquire and analyze image datagenerated at different time points or perspectives. Some examplesinclude tools for analysis of satellite imagery and molecular biologytools for sequencing and characterizing the molecular identity of aspecimen. In any such system, acquiring and storing large numbers ofhigh-quality images typically requires massive amounts of storagecapacity. Additionally, once acquired and stored, the analysis of imagedata can become resource intensive and can interfere with processingcapacity of other functions, such as ongoing acquisition and storage ofadditional image data. As such, methods and systems which improve thespeed and accuracy of analysis of the acquisition and analysis of imagedata would be beneficial.

In the molecular biology field, one of the processes for nucleic acidsequencing in use is sequencing-by-synthesis. The technique can beapplied to massively parallel sequencing projects. For example, by usingan automated platform, it is possible to carry out hundreds of thousandsof sequencing reactions simultaneously. Thus, one of the embodiments ofthe present invention relates to instruments and methods for acquiring,storing, and analyzing image data generated during nucleic acidsequencing. Although the embodiments of the present invention aredescribed in relation to nucleic acid sequencing, they are applicable inany field where image data acquired at different time points, spatiallocations or other temporal or physical perspectives is analyzed. Forexample, the methods and systems described herein are useful in thefields of molecular and cell biology where image data from microarrays,biological specimens, cells, organisms and the like is acquired and atdifferent time points or perspectives and analyzed. Images can beobtained using any number of techniques known in the art including, butnot limited to, fluorescence microscopy, light microscopy, confocalmicroscopy, optical imaging, magnetic resonance imaging, tomographyscanning or the like. As another example, the methods and systemsdescribed herein can be applied where image data obtained bysurveillance, aerial or satellite imaging technologies and the like isacquired at different time points or perspectives and analyzed. Themethods and systems are particularly useful for analyzing imagesobtained for a field of view in which the features being viewed remainin the same locations relative to each other in the field of view. Thefeatures may however have characteristics that differ in separateimages, for example, the features may appear different in separateimages of the field of view. For example, the features may appeardifferent with regard to the color of a given feature detected indifferent images, a change in the intensity of signal detected for agiven feature in different images, or even the appearance of a signalfor a given feature in one image and disappearance of the signal for thefeature in another image.

Enormous gains in the amount of data that can be acquired and storedmake streamlined image analysis methods even more beneficial. Forexample, the image analysis methods described herein permit bothdesigners and end users to make efficient use of existing computerhardware. Accordingly, presented herein are methods and systems whichreduce the computational burden of processing data in the face ofrapidly increasing data output. For example, in the field of DNAsequencing, yields have scaled 15-fold over the course of a recent year,and can now reach hundreds of gigabases in a single run of a DNAsequencing device. If computational infrastructure requirements grewproportionately, large genome-scale experiments would remain out ofreach to most researchers. Thus, the generation of more raw sequencedata will increase the need for secondary analysis and data storage,making optimization of data transport and storage extremely valuable.Some embodiments of the methods and systems presented herein can reducethe time, hardware, networking, and laboratory infrastructurerequirements needed to produce usable sequence data.

Sequencing Methods

The methods described herein can be used in conjunction with a varietyof nucleic acid sequencing techniques. Particularly applicabletechniques are those wherein nucleic acids are attached at fixedlocations in an array such that their relative positions do not changeand wherein the array is repeatedly imaged. Embodiments in which imagesare obtained in different color channels, for example, coinciding withdifferent labels used to distinguish one nucleotide base type fromanother are particularly applicable. In some embodiments, the process todetermine the nucleotide sequence of a target nucleic acid can be anautomated process. Preferred embodiments include sequencing-by-synthesis(“SBS”) techniques.

SBS techniques generally involve the enzymatic extension of a nascentnucleic acid strand through the iterative addition of nucleotidesagainst a template strand. In traditional methods of SBS, a singlenucleotide monomer may be provided to a target nucleotide in thepresence of a polymerase in each delivery. However, in the methodsdescribed herein, more than one type of nucleotide monomer can beprovided to a target nucleic acid in the presence of a polymerase in adelivery.

SBS can utilize nucleotide monomers that have a terminator moiety orthose that lack any terminator moieties. Methods utilizing nucleotidemonomers lacking terminators include, for example, pyrosequencing andsequencing using γ-phosphate-labeled nucleotides, as set forth infurther detail below. In methods using nucleotide monomers lackingterminators, the number of nucleotides added in each cycle is generallyvariable and dependent upon the template sequence and the mode ofnucleotide delivery. For SBS techniques that utilize nucleotide monomershaving a terminator moiety, the terminator can be effectivelyirreversible under the sequencing conditions used as is the case fortraditional Sanger sequencing which utilizes dideoxynucleotides, or theterminator can be reversible as is the case for sequencing methodsdeveloped by Solexa (now Illumina, Inc.).

SBS techniques can utilize nucleotide monomers that have a label moietyor those that lack a label moiety. Accordingly, incorporation events canbe detected based on a characteristic of the label, such as fluorescenceof the label; a characteristic of the nucleotide monomer such asmolecular weight or charge; a byproduct of incorporation of thenucleotide, such as release of pyrophosphate; or the like. Inembodiments, where two or more different nucleotides are present in asequencing reagent, the different nucleotides can be distinguishablefrom each other, or alternatively, the two or more different labels canbe the indistinguishable under the detection techniques being used. Forexample, the different nucleotides present in a sequencing reagent canhave different labels and they can be distinguished using appropriateoptics as exemplified by the sequencing methods developed by Solexa (nowIllumina, Inc.).

Preferred embodiments include pyrosequencing techniques. Pyrosequencingdetects the release of inorganic pyrophosphate (PPi) as particularnucleotides are incorporated into the nascent strand (Ronaghi, M.,Karamohamed, S., Pettersson, B., Uhlen, M. and Nyren, P. (1996)“Real-time DNA sequencing using detection of pyrophosphate release.”Analytical Biochemistry 242(1), 84-9; Ronaghi, M. (2001) “Pyrosequencingsheds light on DNA sequencing.” Genome Res. 11(1), 3-11; Ronaghi, M.,Uhlen, M. and Nyren, P. (1998) “A sequencing method based on real-timepyrophosphate.” Science 281(5375), 363; U.S. Pat. Nos. 6,210,891;6,258,568 and 6,274,320, the disclosures of which are incorporatedherein by reference in their entireties). In pyrosequencing, releasedPPi can be detected by being immediately converted to adenosinetriphosphate (ATP) by ATP sulfurylase, and the level of ATP generated isdetected via luciferase-produced photons. The nucleic acids to besequenced can be attached to features in an array and the array can beimaged to capture the chemiluminescent signals that are produced due toincorporation of a nucleotides at the features of the array. An imagecan be obtained after the array is treated with a particular nucleotidetype (e.g. A, T, C or G). Images obtained after addition of eachnucleotide type will differ with regard to which features in the arrayare detected. These differences in the image reflect the differentsequence content of the features on the array. However, the relativelocations of each feature will remain unchanged in the images. Theimages can be stored, processed and analyzed using the methods set forthherein. For example, images obtained after treatment of the array witheach different nucleotide type can be handled in the same way asexemplified herein for images obtained from different detection channelsfor reversible terminator-based sequencing methods.

In another exemplary type of SBS, cycle sequencing is accomplished bystepwise addition of reversible terminator nucleotides containing, forexample, a cleavable or photobleachable dye label as described, forexample, in WO 04/018497 and U.S. Pat. No. 7,057,026, the disclosures ofwhich are incorporated herein by reference. This approach is beingcommercialized by Solexa (now Illumina Inc.), and is also described inWO 91/06678 and WO 07/123744, each of which is incorporated herein byreference. The availability of fluorescently-labeled terminators inwhich both the termination can be reversed and the fluorescent labelcleaved facilitates efficient cyclic reversible termination (CRT)sequencing. Polymerases can also be co-engineered to efficientlyincorporate and extend from these modified nucleotides.

Preferably in reversible terminator-based sequencing embodiments, thelabels do not substantially inhibit extension under SBS reactionconditions. However, the detection labels can be removable, for example,by cleavage or degradation. Images can be captured followingincorporation of labels into arrayed nucleic acid features. Inparticular embodiments, each cycle involves simultaneous delivery offour different nucleotide types to the array and each nucleotide typehas a spectrally distinct label. Four images can then be obtained, eachusing a detection channel that is selective for one of the fourdifferent labels. Alternatively, different nucleotide types can be addedsequentially and an image of the array can be obtained between eachaddition step. In such embodiments each image will show nucleic acidfeatures that have incorporated nucleotides of a particular type.Different features will be present or absent in the different images duethe different sequence content of each feature. However, the relativeposition of the features will remain unchanged in the images. Imagesobtained from such reversible terminator-SBS methods can be stored,processed and analyzed as set forth herein. Following the image capturestep, labels can be removed and reversible terminator moieties can beremoved for subsequent cycles of nucleotide addition and detection.Removal of the labels after they have been detected in a particularcycle and prior to a subsequent cycle can provide the advantage ofreducing background signal and crosstalk between cycles. Examples ofuseful labels and removal methods are set forth below.

In particular embodiments some or all of the nucleotide monomers caninclude reversible terminators. In such embodiments, reversibleterminators/cleavable fluors can include fluor linked to the ribosemoiety via a 3′ ester linkage (Metzker, Genome Res. 15:1767-1776 (2005),which is incorporated herein by reference). Other approaches haveseparated the terminator chemistry from the cleavage of the fluorescencelabel (Ruparel et al., Proc Nal Acad Sci USA 102: 5932-7 (2005), whichis incorporated herein by reference in its entirety). Ruparel et aldescribed the development of reversible terminators that used a small 3′allyl group to block extension, but could easily be deblocked by a shorttreatment with a palladium catalyst. The fluorophore was attached to thebase via a photocleavable linker that could easily be cleaved by a 30second exposure to long wavelength UV light. Thus, either disulfidereduction or photocleavage can be used as a cleavable linker. Anotherapproach to reversible termination is the use of natural terminationthat ensues after placement of a bulky dye on a dNTP. The presence of acharged bulky dye on the dNTP can act as an effective terminator throughsteric and/or electrostatic hindrance. The presence of one incorporationevent prevents further incorporations unless the dye is removed.Cleavage of the dye removes the fluor and effectively reverses thetermination. Examples of modified nucleotides are also described in U.S.Pat. Nos. 7,427,673, and 7,057,026, the disclosures of which areincorporated herein by reference in their entireties.

Additional exemplary SBS systems and methods which can be utilized withthe methods and systems described herein are described in U.S. PatentApplication Publication No. 2007/0166705, U.S. Patent ApplicationPublication No. 2006/0188901, U.S. Pat. No. 7,057,026, U.S. PatentApplication Publication No. 2006/0240439, U.S. Patent ApplicationPublication No. 2006/0281109, PCT Publication No. WO 05/065814, U.S.Patent Application Publication No. 2005/0100900, PCT Publication No. WO06/064199 and PCT Publication No. WO 07/010251, the disclosures of whichare incorporated herein by reference in their entireties.

Some embodiments can utilize sequencing by ligation techniques. Suchtechniques utilize DNA ligase to incorporate oligonucleotides andidentify the incorporation of such oligonucleotides. Theoligonucleotides typically have different labels that are correlatedwith the identity of a particular nucleotide in a sequence to which theoligonucleotides hybridize. As with other SBS methods, images can beobtained following treatment of an array of nucleic acid features withthe labeled sequencing reagents. Each image will show nucleic acidfeatures that have incorporated labels of a particular type. Differentfeatures will be present or absent in the different images due thedifferent sequence content of each feature, but the relative position ofthe features will remain unchanged in the images. Images obtained fromligation-based sequencing methods can be stored, processed and analyzedas set forth herein. Exemplary SBS systems and methods which can beutilized with the methods and systems described herein are described inU.S. Pat. Nos. 6,969,488, 6,172,218, and 6,306,597, the disclosures ofwhich are incorporated herein by reference in their entireties.

Some embodiments can utilize nanopore sequencing (Deamer, D. W. &Akeson, M. “Nanopores and nucleic acids: prospects for ultrarapidsequencing.” Trends Biotechnol. 18, 147-151 (2000); Deamer, D. and D.Branton, “Characterization of nucleic acids by nanopore analysis”. Acc.Chem. Res. 35:817-825 (2002); Li, J., M. Gershow, D. Stein, E. Brandin,and J. A. Golovchenko, “DNA molecules and configurations in asolid-state nanopore microscope” Nat. Mater. 2:611-615 (2003), thedisclosures of which are incorporated herein by reference in theirentireties). In such embodiments, the target nucleic acid passes througha nanopore. The nanopore can be a synthetic pore or biological membraneprotein, such as α-hemolysin. As the target nucleic acid passes throughthe nanopore, each base-pair can be identified by measuring fluctuationsin the electrical conductance of the pore. (U.S. Pat. No. 7,001,792;Soni, G. V. & Meller, “A. Progress toward ultrafast DNA sequencing usingsolid-state nanopores.” Clin. Chem. 53, 1996-2001 (2007); Healy, K.“Nanopore-based single-molecule DNA analysis.” Nanomed. 2, 459-481(2007); Cockroft, S. L., Chu, J., Amorin, M. & Ghadiri, M. R. “Asingle-molecule nanopore device detects DNA polymerase activity withsingle-nucleotide resolution.” J. Am. Chem. Soc. 130, 818-820 (2008),the disclosures of which are incorporated herein by reference in theirentireties). Data obtained from nanopore sequencing can be stored,processed and analyzed as set forth herein. In particular, the data canbe treated as an image in accordance with the exemplary treatment ofoptical images and other images that is set forth herein.

Some embodiments can utilize methods involving the real-time monitoringof DNA polymerase activity. Nucleotide incorporations can be detectedthrough fluorescence resonance energy transfer (FRET) interactionsbetween a fluorophore-bearing polymerase and 7-phosphate-labelednucleotides as described, for example, in U.S. Pat. Nos. 7,329,492 and7,211,414 (each of which is incorporated herein by reference) ornucleotide incorporations can be detected with zero-mode waveguides asdescribed, for example, in U.S. Pat. No. 7,315,019 (which isincorporated herein by reference) and using fluorescent nucleotideanalogs and engineered polymerases as described, for example, in U.S.Pat. No. 7,405,281 and U.S. Patent Application Publication No.2008/0108082 (each of which is incorporated herein by reference). Theillumination can be restricted to a zeptoliter-scale volume around asurface-tethered polymerase such that incorporation of fluorescentlylabeled nucleotides can be observed with low background (Levene, M. J.et al. “Zero-mode waveguides for single-molecule analysis at highconcentrations.” Science 299, 682-686 (2003); Lundquist, P. M. et al.“Parallel confocal detection of single molecules in real time.” Opt.Lett. 33, 1026-1028 (2008); Korlach, J. et al. “Selective aluminumpassivation for targeted immobilization of single DNA polymerasemolecules in zero-mode waveguide nanostructures.” Proc. Natl. Acad. Sci.USA 105, 1176-1181 (2008), the disclosures of which are incorporatedherein by reference in their entireties). Images obtained from suchmethods can be stored, processed and analyzed as set forth herein.

The above SBS methods can be advantageously carried out in multiplexformats such that multiple different target nucleic acids aremanipulated simultaneously. In particular embodiments, different targetnucleic acids can be treated in a common reaction vessel or on a surfaceof a particular substrate. This allows convenient delivery of sequencingreagents, removal of unreacted reagents and detection of incorporationevents in a multiplex manner. In embodiments using surface-bound targetnucleic acids, the target nucleic acids can be in an array format. In anarray format, the target nucleic acids can be typically bound to asurface in a spatially distinguishable manner. The target nucleic acidscan be bound by direct covalent attachment, attachment to a bead orother particle or binding to a polymerase or other molecule that isattached to the surface. The array can include a single copy of a targetnucleic acid at each site (also referred to as a feature) or multiplecopies having the same sequence can be present at each site or feature.Multiple copies can be produced by amplification methods such as, bridgeamplification or emulsion PCR as described in further detail below.

The methods set forth herein can use arrays having features at any of avariety of densities including, for example, at least about 10features/cm², 100 features/cm², 500 features/cm², 1,000 features/cm²,5,000 features/cm², 10,000 features/cm², 50,000 features/cm², 100,000features/cm², 1,000,000 features/cm², 5,000,000 features/cm², or higher.

It will be appreciated that any of the above-described sequencingprocesses can be incorporated into the methods and/or systems describedherein. Furthermore, it will be appreciated that other known sequencingprocesses can be easily by implemented for use with the methods and/orsystems described herein. It will also be appreciated that the methodsand systems described herein are designed to be applicable with anynucleic acid sequencing technology. Additionally, it will be appreciatedthat the methods and systems described herein have even widerapplicability to any field where tracking and analysis of features in aspecimen over time or from different perspectives is important. Forexample, the methods and systems described herein can be applied whereimage data obtained by surveillance, aerial or satellite imagingtechnologies and the like is acquired at different time points orperspectives and analyzed.

Processing: Image Acquisition and Analysis in Real Time

In accordance with the above, provided herein are methods of performingimage analysis which allow image analysis to occur while acquiringand/or storing large amounts of image data. The methods can includeperforming image analysis in the background of a process thatpreferentially acquires and/or stores image data. Such methods can beperformed by a single processor capable of time-division multiplexing orother multithreading process. Accordingly, image analysis can beperformed using widely available single processor machines to bothacquire and/or store large amounts of image data using one processthread while performing image analysis using another thread. Inpreferred embodiments, the acquisition and/or storage thread takesprecedence over the analysis thread. In such embodiments, the imageanalysis instructions or image analysis program monitors the filecreation and/or write activity taking place during the image acquisitionand/or storage process. When data acquisition and/or storage is notoccurring, the processor is permitted to execute a set of image analysisinstructions or an image analysis program. This can allow for areduction in the total amount of data ultimately exported for storage ordownstream analysis. In other embodiments, such methods are implementedusing multiple processes that may or may not overlap temporally, forexample, by utilizing two or more separate processors.

In particular embodiments, the methods and systems set forth hereinprovide the advantage of reducing the amount of data to a moremanageable level from the amount of data present in raw images. Forexample, the methods and systems can provide at least a 2-fold, at leasta 10-fold, at least a 25-fold, at least a 50-fold or at least a 100-foldreduction in data size when comparing data content of images to dataindicative of a detectable characteristic of the images. An example isthe reduction in data in going from images of an array of nucleic acidsthat are being sequenced to data indicating the identity of a DNAsequence for nucleic acids in the array (e.g. sequence of base calls).

In certain aspects, the methods can include the steps of providing afirst data set to store on a storage device; providing a second data setfor analysis; processing the first data set and the second data set;wherein the processing comprises acquiring and/or storing the first dataset on the storage device and analyzing the second data set when theprocessor is not acquiring and/or storing the first data set. In certainaspects, the processing step includes identifying at least one instanceof a conflict between acquiring and/or storing the first data set andanalyzing the second data set; and resolving the conflict in favor ofacquiring and/or storing image data such that acquiring and/or storingthe first data set is given priority.

As used herein, the terms storage, storage device, storage capacity andthe like can refer to any medium, device or means of storage ofinformation. Storage can include, but is not limited to, a disk drivedevice such as a hard drive, floppy disk, optical or magneto-opticaldisk, memory such as RAM or ROM chips, and any other medium used torecord or store data. In some embodiments, a storage capacity isconnected to a processor which sends information to be recorded on thestorage capacity after it is acquired. In specific embodiments, imagedata is acquired by a system and is recorded on a storage capacity. Inother embodiments, image data is acquired by a system and information isfirst extracted from the image data, and the extracted information isrecorded on a storage capacity.

As used herein, analysis can refer to any manipulations performed ondata. In some embodiments, analysis is performed on raw image data. Inother embodiments, analysis is performed on data that has been processedto some degree. In certain embodiments, analysis can include organizing,aligning and ordering a set of image files or data extracted from imagesfiles. Thus, analysis can include, but is not limited to, thepre-processing of images from a subset of images, tracking the locationof features of a specimen across a set of images of the specimencaptured at different reference points, generation of templates,registration of signal and data to a template, extraction of signal datafrom an image, generation of offset data, calculating a color matrix forsignals in image data, calculation of phasing parameters, assigningcolors to features in an image, correcting the assignment of colors dueto issues such as channel cross-talk and phasing, calculating qualityscores for each color assignment, generating output files, and the like.

In preferred embodiments of the image analysis methods and systemspresented herein, image data for nucleic acid sequencing applications isacquired and analyzed. For example, FIG. 1 describes the general flow ofoperations that can be performed during the acquisition and analysis ofDNA sequencing image data.

As used herein, “acquiring”, “acquisition” and like terms refer to anypart of the process of obtaining an image file. In some embodiments,data acquisition can include generating an image of a specimen, lookingfor a signal in a specimen, instructing a detection device to look foror generate an image of a signal, giving instructions for furtheranalysis or transformation of an image file, and any number oftransformations or manipulations of an image file.

As used herein, “image” refers to a reproduction or representation of atleast a portion of a specimen or other object. In some embodiments, thereproduction is an optical reproduction, for example, produced by acamera or other optical detector. The reproduction can be a non-opticalreproduction, for example, a representation of electrical signalsobtained from an array of nanopore features or a representation ofelectrical signals obtained from an ion-sensitive CMOS detector. Inparticular embodiments non-optical reproductions can be excluded from amethod or apparatus set forth herein. An image can have a resolutioncapable of distinguishing features of a specimen that are present at anyof a variety of spacings including, for example, those that areseparated by less than 100 μm, 50 μm, 10 μm, 5 μm, 1 μm or 0.5 μm.

In preferred embodiments, the methods provided herein can includedetermining whether a processor is actively acquiring data or whetherthe processor is in a low activity state. Acquiring and storing largenumbers of high-quality images typically requires massive amounts ofstorage capacity. Additionally, once acquired and stored, the analysisof image data can become resource intensive and can interfere withprocessing capacity of other functions, such as ongoing acquisition andstorage of additional image data. Accordingly, as used herein, the termlow activity state refers to the processing capacity of a processor at agiven time. In some embodiments, a low activity state occurs when aprocessor is not acquiring and/or storing data. In some embodiments, alow activity state occurs when some data acquisition and/or storage istaking place, but additional processing capacity remains such that imageanalysis can occur at the same time without interfering with otherfunctions.

As used herein, “identifying a conflict” refers to identifying asituation where multiple processes compete for resources. In some suchembodiments, one process is given priority over another process. In someembodiments, a conflict may relate to the need to give priority forallocation of time, processing capacity, storage capacity or any otherresource for which priority is given. Thus, in some embodiments, whereprocessing time or capacity is to be distributed between two processessuch as either analyzing a data set and acquiring and/or storing thedata set, a conflict between the two processes exists and can beresolved by giving priority to one of the processes.

Also provided herein are systems for performing image analysis. Thesystems can include a processor; a storage capacity; and a program forimage analysis, the program comprising instructions for processing afirst data set for storage and the second data set for analysis, whereinthe processing comprises acquiring and/or storing the first data set onthe storage device and analyzing the second data set when the processoris not acquiring the first data set. In certain aspects, the programincludes instructions for identifying at least one instance of aconflict between acquiring and/or storing the first data set andanalyzing the second data set; and resolving the conflict in favor ofacquiring and/or storing image data such that acquiring and/or storingthe first data set is given priority. In certain aspects, the first dataset comprises image files obtained from an optical imaging device. Incertain aspects, the system further comprises an optical imaging device.In some aspects, the optical imaging device comprises a light source anda detection device.

As used herein, the term “program” refers to instructions or commands toperform a task or process. The term “program” can be usedinterchangeably with the term module. In certain embodiments, a programcan be a compilation of various instructions executed under the same setof commands. In other embodiments, a program can refer to a discretebatch or file.

Set forth below are some of the surprising effects of utilizing themethods and systems for performing image analysis set forth herein. Insome sequencing embodiments, an important measure of a sequencingsystem's utility is its overall efficiency. For example, the amount ofmappable data produced per day and the total cost of installing andrunning the instrument are important aspects of an economical sequencingsolution. To reduce the time to generate mappable data and to increasethe efficiency of the system, real-time base calling can be enabled onan instrument computer and can run in parallel with sequencing chemistryand imaging. This allows much of the data processing and analysis to becompleted before the sequencing chemistry finishes. Additionally, it canreduce the storage required for intermediate data and limit the amountof data that needs to travel across the network.

While sequence output has increased, the data per run transferred fromthe systems provided herein to the network and to secondary analysisprocessing hardware has substantially decreased. By transforming data onthe instrument computer (acquiring computer), network loads aredramatically reduced. Without these on-instrument, off-network datareduction techniques, the image output of a fleet of DNA sequencinginstruments would cripple most networks. For example, if a lab chose toexport image files, it could run no more than five instrumentsconcurrently without saturating a standard gigabit connection to asingle file system. Because of the advances in real time data analysispresented herein, huge aggregate sequence output can now be supportedwith conventional network and hardware configurations.

The widespread adoption of the high-throughput DNA sequencinginstruments has been driven in part by ease of use, support for a rangeof applications, and suitability for virtually any lab environment. Thehighly efficient algorithms presented herein allow significant analysisfunctionality to be added to a simple workstation that can controlsequencing instruments. This reduction in the requirements forcomputational hardware has several practical benefits that will becomeeven more important as sequencing output levels continue to increase.For example, by performing image analysis and base calling on a simpletower, heat production, laboratory footprint, and power consumption arekept to a minimum. In contrast, other commercial sequencing technologieshave recently ramped up their computing infrastructure for primaryanalysis, with up to five times more processing power, leading tocommensurate increases in heat output and power consumption. Thus, insome embodiments, the computational efficiency of the methods andsystems provided herein enables customers to increase their sequencingthroughput while keeping server hardware expenses to a minimum.

Accordingly, in some embodiments, the methods and/or systems presentedherein act as a state machine, keeping track of the individual state ofeach specimen, and when it detects that a specimen is ready to advanceto the next state, it does the appropriate processing and advances thespecimen to that state. A more detailed example of how the state machinemonitors a file system to determine when a specimen is ready to advanceto the next state according to a preferred embodiment is set forth inExample 1 below.

In preferred embodiments, the methods and systems provided herein aremulti-threaded and can work with a configurable number of threads. Thus,for example in the context of nucleic acid sequencing, the methods andsystems provided herein are capable of working in the background duringa live sequencing run for real-time analysis, or it can be run using apre-existing set of image data for off-line analysis. In certainpreferred embodiments, the methods and systems handle multi-threading bygiving each thread its own subset of specimen for which it isresponsible. This minimizes the possibility of thread contention.

In preferred embodiments of the methods and systems described herein thefile configuration is preserved on shut down. In such embodiments, uponsystem start up, the system will automatically advance each tile's stateto the latest possible state, based on which files exist on the filesystem. Example 1 below describes one embodiment of this process. Inthis way, the method or system can be interrupted or shut down and thenrestarted without affecting processing. For example, if the method orsystem starts up and detects that a specimen already has a templatefile, and already has an intensity files for the first five cycles, thenit can advance the specimen to the “Waiting to Extract Cycle 6” state.

Template Generation

Also provided herein are methods of for template generation. Templategeneration can be viewed as two processes: signal finding and signalselection. The two processes are discussed below.

Signal finding. Provided herein are methods of tracking the location offeatures of a specimen across a set of images of the specimen capturedat different reference points. These methods can be utilized in aprocess referred to herein as template generation. The methods cancomprise the steps of: (a) selecting a subset of images, wherein theimages of the subset depict signals corresponding to features of thespecimen, and wherein the images of the subset are captured at differentreference points; (b) selecting a primary image from the subset ofimages; and (c) registering the signals depicted in the images of thesubset of images with the signals depicted in the primary image, so asto determine the location of the signals depicted in the images withrespect to each other, thereby producing signal clumps; (d) selecting asignal from each of the signal clumps, thereby forming a template thatpermits the identification of the locations of features of the specimen;and (e) registering remaining images in the set of images with thetemplate.

In particular situations there may be a problem of tracking thelocations of features of a specimen in different color images taken atdifferent cycles when the location of each feature in the specimen isfixed at the different cycles and the color of each feature in thespecimen is changed at the different cycles. The methods and systems setforth herein can provide a solution in which a primary image (forexample from a cycle referred to as golden cycle) is selected from amonga set of images (for example images from several initial cycles) andfeatures are registered from the set of images to the location offeatures in the primary image to form a registration file, wherein theregistration file is used to register images from later cycles.

As used herein, the term “template” refers to a representation of thelocation or relation between signals or features. Thus, in someembodiments, a template is a physical grid with a representation ofsignals corresponding to features in a specimen. In some embodiments, atemplate can be a chart, table, text file or other computer fileindicative of locations corresponding to features. In embodimentspresented herein, a template is generated in order to track the locationof features of a specimen across a set of images of the specimencaptured at different reference points. For example, a template could bea set of x,y coordinates or a set of values that describe the directionand/or distance of one feature with respect to another feature.

As used herein, the term “specimen” can refer to an object or area of anobject of which an image is captured. For example, in embodiments whereimages are taken of the surface of the earth, a parcel of land can be aspecimen. In other embodiments where the analysis of biologicalmolecules is performed in a flow cell, the flow cell may be divided intoany number of subdivisions, each of which may be a specimen. Forexample, a flow cell may be divided into various flow channels or lanes,and each lane can be further divided into 2, 3, 4, 5, 6, 7, 8, 9, 10,20, 30, 40, 50, 6070, 80, 90, 100, 110, 120, 140, 160, 180, 200, 400,600, 800, 1000 or more separate regions that are imaged. One example ofa flow cell has 8 lanes, with each lane divided into 120 specimens ortiles. In another embodiment, a specimen may be made up of a pluralityof tiles or even an entire flow cell. Thus, the image of each specimencan represent a region of a larger surface that is imaged.

It will be appreciated that references to ranges and sequential numberlists described herein include not only the enumerated number but allreal numbers between the enumerated numbers.

As used herein, a “feature” is an area of interest within a specimen orfield of view. When used in connection with microarray devices or othermolecular analytical devices, a feature refers to the area occupied bysimilar or identical molecules. For example, a feature can be anamplified oligonucleotide or any other group of a polynucleotide orpolypeptide with a same or similar sequence. In other embodiments, afeature can be any element or group of elements that occupy a physicalarea on a specimen. For example, a feature could be a parcel of land, abody of water or the like. When a feature is imaged, each feature willhave some area. Thus, in many embodiments, a feature is not merely onepixel.

The distances between features can be described in any number of ways.In some embodiments, the distances between features can be describedfrom the center of one feature to the center of another feature. Inother embodiments, the distances can be described from the edge of onefeature to the edge of another feature, or between the outer-mostidentifiable points of each feature. The edge of a feature can bedescribed as the theoretical or actual physical boundary on a chip, orsome point inside the boundary of the feature. In other embodiments, thedistances can be described in relation to a fixed point on the specimenor in the image of the specimen.

As used herein, a “reference point” refers to any temporal or physicaldistinction between images. In a preferred embodiment, a reference pointis a time point. In a more preferred embodiment, a reference point is atime point or cycle during a sequencing reaction. However, the term“reference point” can include other aspects that distinguish or separateimages, such as angle, rotational, temporal, or other aspects that candistinguish or separate images.

As used herein, a “subset of images” refers to a group of images withina set. For example, a subset may contain 1, 2, 3, 4, 6, 8, 10, 12, 14,16, 18, 20, 30, 40, 50, 60 or any number of images selected from a setof images. In particular embodiments, a subset may contain no more than1, 2, 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 30, 40, 50, 60 or any numberof images selected from a set of images. In a preferred embodiment,images are obtained from one or more sequencing cycles with four imagescorrelated to each cycle. Thus, for example, a subset could be a groupof 16 images obtained through four cycles.

As used herein, a “signal” refers to a detectable event such as anemission, preferably light emission, for example, in an image. Thus, inpreferred embodiments, a signal can represent any detectable lightemission that is captured in an image (i.e., a “spot”). Thus, as usedherein, “signal” can refer to both an actual emission from a feature ofthe specimen, and can refer to a spurious emission that does notcorrelate to an actual feature. Thus, a signal could arise from noiseand could be later discarded as not representative of an actual featureof a specimen.

As used herein, the term “clump” refers to a group of signals. Inparticular embodiments, the signals are derived from different features.In a preferred embodiment, a signal clump is a group of signals thatcluster together. In a more preferred embodiment, a signal clumprepresents a physical region covered by one amplified oligonucleotide.Each signal clump should be ideally observed as several signals (one pertemplate cycle, and possibly more due to cross-talk). Accordingly,duplicate signals are detected where two (or more) signals are includedin a template from the same clump of signals.

As used herein, terms such as “minimum,” “maximum,” “minimize,”“maximize” and grammatical variants thereof can include values that arenot the absolute maxima or minima. In some embodiments, the valuesinclude near maximum and near minimum values. In other embodiments, thevalues can include local maximum and/or local minimum values. In someembodiments, the values include only absolute maximum or minimum values.

As used herein, “cross-talk” refers to the detection of signals in oneimage that are also detected in a separate image. In a preferredembodiment, cross-talk can occur when an emitted signal is detected intwo separate detection channels. For example, where an emitted signaloccurs in one color, the emission spectrum of that signal may overlapwith another emitted signal in another color. In a preferred embodiment,fluorescent molecules used to indicate the presence of nucleotide basesA, C, G and T are detected in separate channels. However, because theemission spectra of A and C overlap, some of the C color signal may bedetected during detection using the A color channel. Accordingly,cross-talk between the A and C signals allows signals from one colorimage to appear in the other color image. In some embodiments, G and Tcross-talk. In some embodiments, the amount of cross-talk betweenchannels is asymmetric. It will be appreciated that the amount ofcross-talk between channels can be controlled by, among other things,the selection of signal molecules having an appropriate emissionspectrum as well as selection of the size and wavelength range of thedetection channel.

As used herein, “register”, “registering”, “registration” and like termsrefer to any process to correlate signals in an image or data set from afirst time point or perspective with signals in an image or data setfrom another time point or perspective. For example, registration can beused to align signals from a set of images to form a template. Inanother example, registration can be used to align signals from otherimages to a template. One signal may be directly or indirectlyregistered to another signal. For example, a signal from image “S” maybe registered to image “G” directly. As another example, a signal fromimage “N” may be directly registered to image “G”, or alternatively, thesignal from image “N” may be registered to image “S”, which haspreviously been registered to image “G”. Thus, the signal from image “N”is indirectly registered to image “G”.

In some embodiments, acquired signal data is transformed using an affinetransformation. In some such embodiments, template generation makes useof the fact that the affine transforms between color channels areconsistent between runs. Because of this consistency, a set of defaultoffsets can be used when determining the coordinates of the features ina specimen. For example, a default offsets file can contain the relativetransformation (shift, scale, skew) for the different channels relativeto one channel, such as the A channel. In other embodiments, however,the offsets between color channels drift during a run and/or betweenruns, making offset-driven template generation difficult. In suchembodiments, the methods and systems provided herein can utilizeoffset-less template generation, which is described further below.

FIGS. 2A and 2B demonstrate direct and indirect registration duringoffset-less template generation. As set forth in FIG. 2A, the imagesfrom various images obtained from various cycles (e.g. images identifiedas A, C, T and G to represent the nucleotide type detected in therespective image) can be aligned and then merged. For example,offset-less template generation can proceed by first finding the signalsin image A from cycle 1 (“A1”), using that as a reference and aligningit to the signals in image C from cycle 1 (“C1”), merging the signals toform a new template, then finding the signals in A2, aligning andmerging, then C2, G1, T1, G2 and T2.

In practice, the offset-less template generation procedure set forth inFIG. 2A may not take into account various factors that can decrease thereliability of the resulting template. For example, sometimes the imagesfrom an initial cycle can be flawed due to focus failure of the opticalimaging device. As such, in some embodiments, a different cycle ischosen as a frame of reference. In some cases, it is possible forcross-talk to occur between two channels, thus making it possible toregister those two channels against each other in the same cycle.However, in general, registering an image against another image from thesame cycle is not feasible because, for example, A1 may not registeragainst T1 and G1 may not always register against T1. Thus, at eachregistration step, it is desirable to ensure that the fraction of sharedspots is as high as possible.

Additionally, the offset-less template generation procedure set forth inFIG. 2A can result in other challenges. First, the master template canbecome very dense, particularly in a high-density image, making it moredifficult to register later images against the master template. Second,the wrong cluster might be kept. Specifically, if two signal clumps areidentified at nearly the same position, the one with highsignal-to-noise is more likely to be real, and more likely to havecorrect coordinates. Thus, template generation can prefer highsignal-to-noise clumps to low signal-to-noise clumps. However, in theexemplary procedure set forth in FIG. 2A, clumps from A1 are alwayskept, even if they are dominated by clumps in later cycles. Finally, theexemplary procedure of FIG. 2A does not deal well with shear seen intemplate cycles.

In view of the foregoing, an improved method of generating a templatefor image registration is provided herein, and an example is set forthin FIG. 2B. The method can comprise the steps of: (a) selecting a subsetfrom a set of images of a specimen, the subset of images comprising aplurality of image cycles, wherein the images of the subset depictsignals corresponding to features of the specimen, and wherein theimages of the subset are captured at different reference points; (b)selecting a primary image from the subset of images, wherein the primaryimage is the image having the most signals from the subset of images;(c) selecting a secondary image from the subset of images, wherein thesecondary image is the image having the second most signals from thesubset of images; (d) registering the signals from the secondary imagewith the signals from the primary image; (e) registering signals fromnonselected images with the signals from the secondary image, whereinthe nonselected images are obtained from the image cycle from which theprimary image was obtained; (f) registering signals from remainingimages in the subset with signals from the primary image, so as todetermine the location of the signals depicted in the images withrespect to each other, thereby producing signal clumps; and (g)selecting a signal from each of the signal clumps, thereby forming atemplate that permits the identification of the locations of features ofthe specimen.

In contrast to template generation methods that register images againstan image obtained from a pre-determined cycle, some of the templategeneration methods described herein perform a preliminary evaluation ofseveral cycles of image data (images from a subset of images) in orderto select the cycle image having the most signals from images of theimage subset. The example shown in FIG. 2B sets forth the selection of aprimary image.

As used herein, a “primary image” can refer to either a single image ora compilation of merged images. Thus, in preferred embodiments a primaryimage can be selected as a reference image upon which other images areregistered.

According to the template generation method depicted in FIG. 2B, aprimary image or “golden” cycle g is selected, which is the templatewith the most signals in A and C channels combined. In this example, theA and C channels are selected due to the cross talk that occurs betweenthe channels. The signals from channel Cg are registered against Ag, andthen Ag and Cg are merged to form a reference (A+C)g. A second or“silver” cycle s is selected which is the template with the second-mostsignals in the A and C channels combined. The signals from channel Csare merged with the signals from As, and then the combined (A+C)s isregistered against (A+C)g. Next, Gg and Tg are registered against(A+C)s, thus being indirectly registered to (A+C)g. Next, all otherimages (An, Cn, Gn, Tn) are registered against (A+C)g. Finally, theimage lists are merged together at the end of the procedure, and signalswith higher signal-to-noise values can be saved.

A more detailed example of template generation according to a preferredembodiment is set forth in Example 2 below. It will be understood thatvarious labels and optical components can be used in accordance with themethods set forth herein such that cross talk may occur between channelsother than those identified herein as the A and C channels. Accordingly,different images can be used in ways similar to those exemplified hereinwith regard to the A and C images.

Registration for template generation from dense images. In someembodiments, clusters occur at a high density during a sequencing run.For example, in some embodiments, certain densities can approach orexceed 1,000,000 clusters/mm². The clusters in exemplary embodiments canbe about 1 micometer in diameter. Accordingly, a majority of the surfacecan be occupied by clusters. As such, a majority of the space in animage of the surface can produce signals from clusters. Under certainconditions, template registration may fail at these higher densities.Accordingly, steps that can fail most often are registration of G and Tvirtual images against A and/or C virtual images from other cycles.

However, presented herein is the surprising finding that channels thatdo not cross-talk can be registered against each other in the samecycle. For example, in some embodiments, G and T channels can beregistered against the A channel in the same cycle. Specifically, asdiscussed above, the cross-correlation between the C and A images allowsregistration of the C images against the A image, because when a C imageis overlaid on the A image correctly, many of the clusters appear inboth images. However, the cross-correlation between G and A (or T and A)differs because when G is overlaid on A correctly, clusters do notappear in both images. Furthermore, the cross-correlation for a correctregistration is so low that it is a strong indicator of a signal. Thus,it has been advantageously found that an alternative method forregistration can be implemented by determining the minimum or a lowcross-correlation between channels which do not cross-talk in the samecycle (e.g., G+A, G+C, T+A, and/or T+C).

Accordingly, this surprising finding provides for marked improvements inregistration of G and T images over a range of densities. For example,registration among non-cross-talking channels can occur over a rangefrom about 10 clusters/mm², to about 1,000,000 clusters/mm². In typicalembodiments, registration among non-crosstalking channels can occur overa range from about 100,000 clusters/mm² to about 1,000,000 clusters/mm².In such embodiments, the clusters can be any of a variety of shapes orsizes, for example, being roughly circular and having a diameter ofabout 1 micrometer. Registration among non-crosstalking channels canoccur, for example, when at least about 25%, 50%, 60%, 70%, 80% or 90%of the space in a square millimeter of a surface or image thereof isoccupied by clusters. Reference to an image here, as elsewhere in thisdisclosure, can include a composite image in which signals from multiplechannels are present such that the image shows all or most of theclusters on the surface that was imaged.

Signal Selection. As described above, template generation can be viewedas two processes: signal finding and signal selection. Signal finding isdescribed above. Signal selection refers to a process of orderingsignals, adding them to a template and discarding any signal whichalready has a template neighbor within a defined cluster radius.Accordingly, also provided herein are methods of selection of signals,also referred to elsewhere herein as spot selection. These include amethod of selecting a signal from a cluster of signals in an image. Themethod can comprise the steps of: discarding any signals that do notappear within a cluster of signals, the cluster of signals having adefined cluster radius; and selecting the signal with the highestintensity of signals within a cluster. In certain aspects, the selectingstep further comprises discarding any signal which already has a signalneighbor within the cluster radius.

In some embodiments, a cluster or clump of signals can comprise one ormore signals or spots that correspond to a particular feature. When usedin connection with microarray devices or other molecular analyticaldevices, a cluster can comprise one or more signals that together occupythe physical region occupied by an amplified oligonucleotide (or otherpolynucleotide or polypeptide with a same or similar sequence). Forexample, where a feature is an amplified oligonucleotide, a cluster canbe the physical region covered by one amplified oligonucleotide. Inother embodiments, a cluster or clump of signals need not strictlycorrespond to a feature. For example, spurious noise signals may beincluded in a signal cluster but not necessarily be within the featurearea. In a preferred embodiment, a feature is observed as severalsignals, with one signal per template cycle, and possibly more due tocross-talk. For example, a cluster of signals from four cycles of asequencing reaction would comprise at least four signals.

As used herein, the term “cluster radius” refers to a defined radiuswhich encompasses a cluster of signals. Accordingly, by defining acluster radius as larger or smaller, a greater number of signals canfall within the radius for subsequent ordering and selection. A clusterradius can be defined by any distance measure, such as pixels, meters,millimeters, or any other useful measure of distance.

In some embodiments, the selecting step further comprises ordering thesignals. Signals can be ordered, for example, by detection count, byintensity or brightness relative to neighboring signals, by chastity, orby any other useful ordering mechanism that allows duplicate signals tobe identified. In certain embodiments signals can be ordered first bydetection count, then by intensity. In other embodiments, signals can beordered first by intensity, then by detection count.

In a preferred embodiment, signals can be ordered by detection count.Detection count refers to the number of times where a signal wasdetected within a given radius of a defined position. The radius can bedefined by any distance measure, such as pixels, meters, millimeters, orany other useful measure of distance. For example, in a preferredembodiment, detection count comprises the number of cycles where asignal was detected within a 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 orgreater than 10.0 pixel radius. In a more preferred embodiment, theradius is 0.5 pixels; however, it will be appreciated that the radiusmay be larger or smaller depending on a variety of factors whichinclude, but are not limited to, the quality of data acquisition and/orthe end user's needs.

In another preferred embodiment, signals can be ordered according tointensity or brightness. The intensity or brightness of a signal can bedefined relative to other signals, neighboring or background pixels, orby any other useful mechanism that allows for two or more signals to beordered according to intensity or brightness. For example, in apreferred embodiment, the brightness of a signal can be defined asbrightness relative to neighboring pixels, brightness relative to anoise estimate, absolute brightness, brightness as a percentile, orextracted (bilinear) brightness. In certain preferred embodiments, thebrightness is calculated as the brightness relative to neighboringpixels. However, it will be apparent to one of skill in the art thatother suitable methods of determining the brightness of a signal can beused for ordering signals.

In addition to the ordering mechanisms described above, it will beapparent that other suitable mechanisms can be used for ordering signalsacross multiple images. For example, the ordering mechanism can be thenumber of signals identified within a given radius. In certainembodiments, the radius can be a 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0 orgreater than a 10.0 pixel radius. The radius can be defined by anydistance measure, such as pixels, meters, millimeters, or any otheruseful measure of distance. In other embodiments, the mechanism can bethe distance to nearest neighbor, the distance to nearest neighbor fromanother cycle, or the distance to the nearest three neighbors.

One factor that can affect template generation is the overall density offeatures in a given specimen or image. In embodiments where the densityis high, the defined cluster radius can be lowered in response toincreased signal density. Accordingly, depending on the density of datain a specimen, the defined radius can be reduced. Where the radius isdefined in pixels, the radius can be reduced to 2.0 pixels, 1.5 pixels,1.0 pixel or less than 1.0 pixel.

A more detailed example of signal selection according to a preferredembodiment is set forth in Example 3 below.

Merging Signals Using Chastity Determination. During templategeneration, signals (spots) can be identified on each image in eachtemplate cycle. Spots which correspond to the same cluster can be mergedtogether. It will be appreciated that two separate issues can arise whenmerging spots. First, if two spots from the same cluster are not merged,duplicate reads of the same feature will result. Second, if spots fromdifferent clusters are merged together, which results in the loss of oneof the spots, the template will omit features. This loss of features isespecially prone to happen for small clusters. Advantageously, thedetermination of whether to merge or keep a spot can be markedlyimproved by determining the chastity of a spot (e.g., the intensity ofthe spot relative to a nearby spot) and using that chastity value insubsequent determinations.

Thus, some embodiments of the present disclosure relate to proceduresfor evaluating whether two spots should be merged. In certainembodiments, the procedure can comprise one or more of the followingsteps. 1) After generating a list of identified spots, intensities foreach template cycle are extracted. 2) A cross-talk correction matrix iscomputed, and a preliminary base call is made for each spot. 3) Thechastity of each base call is computed, as described below. 4) Spotswith 2 or more chastity failures (where chastity is less than athreshold value, for example, less than 0.6) are omitted. 5) The samplediversity is determined, as is the probability that clusters will matchbase calls by chance. 6) The following analysis is performed initeration for spots from highest to lowest chastity over templatecycles. For each spot, if there is already a spot in the template withinradius r₁, the spot is discarded. If there is already a spot in thetemplate within radius r₂, and all reliable base calls (for example,chastity >0.7), the spot is discarded. In particular, if there are nothigh-chastity base calls, the spot is discarded. Otherwise, the spot isadded to the template.

In the above embodiments, chastity can be calculated using the followingexemplary method. Let A designate the maximum channel signal for acluster in a cycle, and B designate the second highest signal. In theevent that B is negative, 1-B is added to both A and B, to forcenon-negative values. Chastity is defined as A/(A+B). Using thisdefinition, chastity ranges from a minimum of 0.5 to a maximum of 1.Higher-chastity calls are more likely to be correct. Thus, in someembodiments, clusters which have two or more base calls with chastityvalue less than a threshold value, for example less than 0.6 in thefirst 25 cycles are filtered.

In one example, two spots are considered as matching if, for eachtemplate cycle, (a) they have the same base call or (b) one or bothspots have chastity <0.7. Further, the odds, M, that tworandomly-selected spots will match are then computed as follows. Let nbe the number of template cycles (typically 4 or 5). All strings oflength n consisting of the bases A, C, G, T, N are indexed. For sequencei, let p_(i) be the fraction of clusters with this sequence. Let C_(ij)equal 1 if sequences i and j match, and 0 otherwise. Thus, C_(ii)=1where all sequences are i. Then M is calculated:

$M = {\sum\limits_{i,j}{p_{i}p_{j}C_{ij}}}$

In typical embodiments, M is below 1% for high-quality genomic data. Mis higher when diversity is low or when data quality leads to lowchastity.

Thus, based on the calculated value of M, clusters can be merged usingchastity and base call information combined or chastity alone.Accordingly, in some embodiments where M is low (e.g., under about 20%),clusters are merged based on chastity and base calls combined. In someembodiments where M is high (e.g., greater than about 20%), clusters aremerged based on chastity alone.

Registration and Intensity Extraction

As described above, the process of aligning the template of signalpositions onto a given image is referred to as registration, and theprocess for determining an intensity value for each signal in thetemplate for a given image is referred to as intensity extraction. Forregistration, the methods and systems provided herein take advantage ofthe random nature of signal clump positions by using image correlationto align the template to the image.

After a template has been generated using the methods described above,images can be registered to the template. Some of the basic stepsinvolved in registration include loading the reference template for agiven specimen and loading image files for one or more channels for agiven imaging cycle. Then, for each image, an x,y shift can beidentified. This can be done in any number of ways. In a preferredembodiment, an x,y shift is identified by correlating the location of afixed point in the image with a fixed coordinate in the template. In amore preferred embodiment, the fixed point is one or more corners of theimage, or one or more subregions near the corners of the image. The x,yshift information is then used to align the template and the image. Thisalignment can be performed using any suitable mechanism. In a preferredembodiment, the alignment uses a 6-parameter affine transformation thattransforms the template positions into the image coordinates. In a morepreferred embodiment, the 6-parameter affine transformation utilizes thex,y shift information for each of four subregions.

An intensity value for each signal in the template for a given image isthen determined. In some embodiments, the intensity value is estimatedusing mechanisms that enhance signal to noise. For example, in apreferred embodiment, for each transformed cluster position, bilinearinterpolation is used to estimate the intensity value of the clusterfrom a Laplacian pre-sharpened version of the image. The background canthen be subtracted. In a preferred embodiment, the background isestimated from a region including and surrounding the cluster. Forexample, the region can be 32×32 pixels, or any suitably sized regionsurrounding and/or including the cluster itself. Estimates of backgroundcan be generated using any suitable method. In a preferred embodiment,estimates are generated based on the average of the dimmest four pixelsin each region, and the subtraction interpolates between various regionsto remove discontinuities in the estimate. However, it should beapparent to one of skill in the art that any suitable method ofestimating background can be utilized.

Once background has been estimated and subtracted, the intensity valuesextracted across a specimen can be normalized to account for varianceacross a specimen. Thus, in a preferred embodiment, the intensity valuesfor sub-tiles are normalized such that the 90, percentile of theirextracted intensities are equal. A sub-tile can be any suitable portionof a tile. For example, a tile can be divided using a grid format. Thegrid can be any suitable size that allows intensity values to benormalized. In a preferred embodiment, tiles are divided into a 4×4 gridof subtiles. Finally, intensity values are saved for future analysis andprocessing.

Color Matrix Estimation and Refinement

In certain embodiments, emission spectra overlap between differentsignals (i.e. “cross-talk”). For example, during sequencing bysynthesis, the four dyes used in the sequencing process typically havesome overlap in emission spectra. Thus, provided herein is a colormatrix that corrects for this cross-talk. The terms color matrix,cross-talk matrix, and like terms as used herein refer to a matrix usedto correct for the cross-talk between channels. For example, when acluster emits a signal in the “C” channel, some of its light is alsocollected in the “A” channel. Thus, a color matrix corrects for thiscross-talk, yielding the intensity generated from each of the fourlabeled nucleotides. The color matrix can also normalize the relativeintensities collected for each of the four nucleotides.

In particular embodiments, a problem of assigning a color (for example,a base call) to different features in a set of images obtained for acycle when cross talk occurs between different color channels and whenthe cross talk is different for different sets of images can be solvedby making a preliminary color matrix for the features, making apreliminary color assignment (such as a base call) based on the colormatrix, evaluating quality of the preliminary color assignment (such asa base call), and refining the preliminary color matrix based on thecolor assignment to form a refined color matrix.

Typically, the color matrix is computed using data from early cycles,and is applicable to all data subsequently generated during a sequencingrun. However, as discussed in greater detail herein, the adaptive matrixprocedure can re-adjust the color matrix to account for shifts inrelative intensities from cycle to cycle or from tile to tile.

For example, in a preferred embodiment, color matrix estimation occursafter a tile has had its cycle 4 (fourth cycle) images registered andextracted. The estimation can, for example, follow the proceduredescribed in “An estimate of the cross-talk matrix in four dyefluorescence-based DNA sequencing” by L. Li, T. P. Speed(Electrophoresis 1999 June; 20(7):1433-42, the content of which ishereby incorporated by reference in its entirety).

Color matrix, however, may change over various reference points, andthus a color matrix may not remain reliable throughout the entireprocess of image acquisition. As such, provided herein are methods andsystems for assigning colors to features in an image, and for refiningthe assignment of colors. In some embodiments, the methods can comprisethe steps of: (a) determining a preliminary color matrix for a feature;(b) evaluating quality of the preliminary color assignment; (c) refiningthe preliminary color matrix based on the color assignment, therebyforming a refined color matrix; and (d) making a final color assignmentfor a feature based on the refined color matrix. In certain aspects, themethods can further comprise the step of making a preliminary colorassignment for a feature based on the preliminary color matrix, whereinthe preliminary color assignment is made prior to step (b).

In certain aspects, the refined color matrix corrects for cross-talkbetween detection channels. In certain aspects, step (c) comprisesre-orthogonalizing signal intensities for the preliminary color matrixat one or more features. In certain aspects, step (c) comprisesre-normalizing signal intensities for the preliminary color matrix atone or more features. In certain aspects, step (c) comprising bothre-orthogonalizing and re-normalizing signal intensities for thepreliminary color matrix at one or more features.

The process of color matrix estimation can be performed using anysuitable algorithm. In a preferred embodiment, the implemented algorithmcan include one or more of the following. For a set of clusters, thescatter plot of objects in two colors is selected, and an axis isselected. The clusters are sorted into bins from the 70^(th) to the90^(th) percentile of intensity along that axis. In the other channel, aline is fit through the 10^(th) percentile intensity of the clusters ineach bin. The slope of this line is the matrix element between the twochannels. This procedure can then be repeated for the other 11combinations of intensities, thus filling the 4×4 matrix, where thediagonal elements are “1”. The intensity distribution can be normalizedby fitting a scale factor so that the Kolmogorov Smirnov distancebetween the different colors is minimized. This process minimizes themismatch across all possible percentiles of the distribution.Alternatively, one could normalize to a specific percentile (e.g., the90^(th) percentile). Then, for the final degree of freedom, enforce thatthe determinant of the color matrix is equal to 1.

The following is an illustration of how a color matrix can be utilizedto correct for cross-talk between channels. Specifically, the colormatrix, M, can allow for a computation of how much intensity is observedin a channel for an underlying signal. In this illustration, a givensignal s has an observed intensity y. Thus, Ms=y. The matrix is used torecover the underlying signal: s=M⁻¹y. Thus, in a typical matrix forthis illustration (shown in the table below), the largest cross-talkeffects (shown in bold) are: 1) the A dye has significant cross-talkinto the C channel, and 2) the G dye has significant cross-talk into theT channel.

Intensity in Channel: A C G T A 1.4 0.3 0 0 C 0.9 0.9 0 0 G 0 0 1.3 0 T0 0 0.8 0.9

Accordingly, for each pair of channels, the intensities in the twochannels are converted to polar coordinates (r, θ). A radius-weightedhistogram is computed of angles θ. The two local maxima in the histogramare identified, and these local maxima give approximate cross-talkcoefficients:

-   -   Tan(θ₁)=C/A (observed C intensity divided by observed A        intensity, for A nucleotide)    -   A Tan(θ₁)=C    -   Tan(θ₁) is the A-to-C cross-talk coefficient (row 2, column 1 of        the matrix)    -   Tan(90-θ₂)=A/C (observed A intensity divided by observed C        intensity, of A nucleotide)    -   Tan(90-θ₂) is the C-to-A cross-talk coefficient

To normalize channels, cross-talk is corrected using the initial matrix,and preliminary base calls are made. High-chastity base calls for eachnucleotide are then identified. After or during base calling, acomputation is made of the 10^(th), 20 ^(th), . . . to the 90^(th)percentiles of A₁, . . . , A₉ of the called A intensities. Similarly, acomputation is made of the percentiles of C_(i), G_(i), T_(i) of thecalled C, G and T intensities. With these computations in hand, anormalization factor for the C channel can be computed by scaling allpercentages to match those of the A channel. The following formula showsthe normalization factor for the C channel. Similar normalizationfactors are computed for the G and T channels.

${NC} = {\frac{1}{9}{\sum\limits_{i = 1}^{9}\frac{A_{i}}{C_{i}}}}$

Finally, the overall matrix is scaled to a determinant=1. With thisnormalization of channels, it is possible to base call by identifyingthe channel with maximum corrected intensity.

In a preferred embodiment, after all tiles have had their color matrixestimated, the methods and system provided herein can compute a medianmatrix across all tiles. In preferred embodiments, this is the matrixthat will be used during an entire sequencing run to generate thecorrected intensities. The median is calculated element by element.However, if a control lane is specified, then the system may only usetiles from that lane for calculating the median matrix that will beused.

Phasing Estimation

A phasing estimation is an analytical tool for reducing noise duringmultiple cycles of a sequencing run. For example, in any given cycle ofa sequencing run, one or more molecules may become “phased” at eachcycle. As used herein, “phased”, “phasing” and like terms refer to thesituation where a molecule at a feature falls at least one base behindother molecules at the same feature as a result of the feature beingsequenced at a particular cycle. As used herein, “pre-phased”,“pre-phasing” and like terms refer to the situation where a molecule ata feature jumps at least one base ahead of other molecules at the samefeature as a result of the feature being sequenced at a particularcycle.

The methods and systems provided herein can assume that a fixed fractionof molecules at each feature become phased and/or pre-phased at eachcycle, in the sense that those molecules fall one base behind insequencing. Thus, in a preferred embodiment, a phasing estimation isperformed to adjust the observed intensities in a way that reduces thenoise created by phased molecules. In a preferred embodiment, a phasingmatrix is created to model phasing effects at any given cycle. This canbe done, for example, by creating an N×N matrix where N is the totalnumber of cycles. Then, to phase-correct intensities for a given cycle,the inverse of the phasing matrix is taken and the matrix rowcorresponding to the cycle is extracted. As a result, the vector ofactual intensities for cycles 1 through N is the product of phasingmatrix inverse and observed intensities for cycles 1 through N.

A more detailed example of a phasing estimation is set forth in Example4 below.

Base Calling

Presented herein are methods and systems for identifying a nucleotidebase in a nucleic acid sequence, or “base calling.” Base calling refersto the process of determining a base call (A, C, G, T) for every featureof a given tile at a specific cycle. In order to base-call, intensitiescan first be corrected for channel cross-talk and for phasing andpre-phasing. The pre-phasing correction implies that base calling willtypically lag intensity extraction, as knowledge of future cycles'intensities are required in order to correct for pre-phasing.

In order to perform base calling in many embodiments, the color matrixmust already be estimated, the phasing and pre-phasing must beestimated, and the intensities for the next few cycles (actual numberdetermined by the length of the phasing window) must exist. In addition,for such embodiments, base calling can take lower priority thanregistration and extraction. That is, base calling will not occur for atile if it is possible to register and extract some subsequent cycle.The reason for this is to remove images from the local hard drive asquickly as possible, since these image files can fill up the localdrive. The images are not needed after extraction and so can be deletedonce extraction occurs.

In some embodiments, base calling can proceed as follows. The relevantintensity files for neighboring cycles (determined by the size of thephasing window) are loaded and color-corrected using the color matrix.Those values are then used to determine a phasing-corrected intensityvector for the current cycle. Each feature will receive a call based onthe brightest phasing-corrected intensity for that feature. Once apreliminary base call has been made, the methods and systems providedherein can determine a refined color matrix for that feature,re-orthogonalize and re-normalize the intensities, and use the fullycorrected intensities to make a final base call.

In view of the foregoing, provided herein are methods of identifying anucleotide base in a nucleic acid sequence. The methods can comprise:determining the intensity of each differently colored signal present ata particular feature on an array at a particular time, wherein eachdifferently colored signal corresponds to a different nucleotide base;repeating the determining step for a plurality of features on an arrayat the particular time; refining the signal intensity for the pluralityof features then repeating the determining step for the plurality offeatures; and selecting for each feature of the plurality of featuresthe refined signal having the highest intensity, wherein the signalhaving the highest intensity at a particular feature corresponds theidentity of the nucleotide base present at the particular feature.

“Refining the signal intensity” refers to modifying the signalintensities for a plurality of features. In preferred embodiments,refining the signal intensity can include re-orthogonalizing orre-normalizing the signal intensities for the plurality of features. Ina more preferred embodiment, refining the signal intensities can includeboth re-orthogonalizing and re-normalizing the signal intensities forthe plurality of features.

Quality Scoring

Quality scoring refers to the process of assigning a quality score toeach base call. Accordingly, presented herein are methods and systemsfor evaluating the quality of a base call from a sequencing read. Insome embodiments, the methods can comprise the steps of: (a) calculatinga set of predictor values for the base call; (b) using the predictorvalues to look up a quality score in a quality table.

The quality score can be presented in any suitable format that allows auser to determine the probability of error of any given base call. Insome embodiments, the quality score is presented as a numerical value.For example, the quality score can be quoted as QXX where the XX is thescore and it means that particular call has a probability of error of10^(−XX/10). Thus, as an example, Q30 equates to an error rate of 1 in1000, or 0.1% and Q40 equates to an error rate of 1 in 10,000 or 0.01%.

In some embodiments, the quality table is generated using Phred scoringon a calibration data set, the calibration set being representative ofrun and sequence variability. Phred scoring is described in greaterdetail in U.S. patent application Ser. No. 12/565,341 filed on Sep. 23,2009 and entitled, “METHOD AND SYSTEM FOR DETERMINING THE ACCURACY OFDNA BASE IDENTIFICATIONS,” the content of which is incorporated hereinby reference in its entirety.

As stated above, quality scoring is performed by calculating a set ofpredictors for each base call, and using those predictor values to lookup the quality score in a quality table. In some embodiments, thequality table is generated using a modification of the Phred algorithmon a calibration data set representative of run and sequencevariability. The predictor values for each base call can be any suitableaspect that may indicate or predict the quality of the base call in agiven sequencing run. For example, some predictors can includeapproximate homopolymer; intensity decay; penultimate chastity; signaloverlap with background (SOWB); and shifted purity G adjustment.

As used herein, “approximate homopolymer” refers to a calculation of thenumber of consecutive identical base calls preceding a base call. Incertain embodiments, the calculation can allow one exception, in orderto identify problematic sequence contexts such as homopolymer runs andproblematic motifs such as “GGCGG”.

As used herein, “intensity decay” refers to the identification of basecalls that suffer loss of signal as sequencing progresses. For example,this can be done by comparing the brightest intensity at the currentcycle to the brightest intensity at cycle 1.

As used herein, “penultimate chastity” refers to measurement of earlyread quality in the first 25 bases based on the second worst chastityvalue. Chastity can be determined as the highest intensity value dividedby the sum of the highest intensity value and the second highestintensity value, where the intensity values are obtained from four colorchannels.

As used herein, “signal overlap with background” (SOWB) refers to ameasurement of the separation of the signal from the noise in previousand subsequent cycles. In a preferred embodiment, the measurementutilizes the 5 cycles immediately preceding and following the currentcycle.

As used herein, “Shifted Purity G adjustment” refers to a measurement ofthe separation of the signal from the noise for the current base callonly, while also accounting for G quenching effects. Due to aninteraction between the dye and the DNA base incorporated in theprevious cycle, the intensities in certain color channels may bedecreased (quenched) in cycles following those cycles where a Gnucleotide was incorporated. In some embodiments, the measurement of theseparation of signal from noise is adjusted for G quenching bymultiplying T channel intensity for a cycle following a G incorporationby 1.3, and by multiplying A channel intensity for a cycle following a Gincorporation by 1.05.

After calculating quality scores, additional operations can beperformed. Thus, in some embodiments, the method for evaluating thequality of a base call further comprises discounting unreliable qualityscores at the end of each read. In preferred embodiments, the step ofdiscounting unreliable quality scores comprises using an algorithm toidentify a threshold of reliability. In a more preferred embodiment,reliable base calls comprise q-values above the threshold and unreliablebase calls comprise q-values below the threshold. An algorithm fordetermining a threshold of reliability can comprise the End AnchoredMaximal Scoring Segments (EAMSS) algorithm, for example. As used herein,an “EAMSS algorithm” is an algorithm that identifies transition pointswhere good and reliable base calls (with mostly high q-values) becomeunreliable base calls (with mostly low q-values). The identification ofsuch transition points can be done, for example, using a Hidden MarkovModel that identifies shifts in the local distributions of qualityscores. For example, a Hidden Markov Model can be used. Useful HiddenMarkov Models are described, for example, in Lawrence R. Rabiner(February 1989). “A tutorial on Hidden Markov Models and selectedapplications in speech recognition”. Proceedings of the IEEE 77 (2):257-286. doi:10.1109/5.18626. However, it will be apparent to one ofskill in the art that any suitable method of discounting unreliablequality scores may be employed. In a preferred embodiment, unreliablebase calls can include base calls with a strong bias toward G basecalls.

Another additional operation that can be performed includes identifyingreads where the second worst chastity in the first 25 base calls isbelow a pre-established threshold, and marking the reads as poor qualitydata. This is referred to as read filtering. As discussed above,chastity can be determined as the highest intensity value divide by thesum of the highest intensity value and the second highest intensityvalue, where the intensity values are obtained from four color channels.

In some embodiments, because some of the above-described predictorsutilize corrected intensities from future cycles, Quality Scoring willtypically lag Base Calling. In a preferred embodiment, a tile is readyfor Quality Scoring if a base call file exists for that cycle and if thecorrected intensity files exist for the next few cycles (determined bythe complexity of the predictors).

Real Time Metrics

The methods and systems provided herein can also utilize real-timemetrics to display run quality to a user. Metrics can be displayed asgraphs, charts, tables, pictures or any other suitable display methodthat provides a meaningful or useful representation of some aspect ofrun quality to a user. For example, real-time metrics displayed to auser can include a display of intensity values over the cycles of a run,the quality of the focus of optical equipment and cluster density ineach lane. Additional metrics displays can include Q score, shown as adistribution based on the Q score, or as a heat map on a per cyclebasis, for example. In some embodiments, real time metrics can include asummary table of various parameters, sorted by, for example, lane, tile,or cycle number. Image data from an entire tile or subregion of a tilemay be displayed for a visual confirmation of image quality. Such imagedata may include close-up, thumbnail images of some or all parts of animage.

Additionally, some metrics displays can include the error rate on aper-cycle basis. The error rate can be calculated using a controlnucleic acid, as described in greater detail below.

A more detailed example of various real-time metrics is set forth inExample 5 below.

Control Nucleic Acids

Also provided herein are methods for verifying that sequence dataobtained from a plurality of arrays is non-artifactual. In molecularbiology embodiments, the method can comprise incorporating a controlnucleic acid into one or more arrays of the plurality of arrays andverifying that the control nucleic acid has been properly sequenced. Ina preferred embodiment, the entire area of each of the one or morearrays comprises replicates of the control nucleic acid. In preferredembodiments, the control nucleic acid has a known sequence. It will beapparent to one of skill in the art that any known and stable sequencecan be used as a control nucleic acid, so long as the sequence issufficiently distinct from sequences of the target nucleic acid to bedistinguished in the sequencing methods being employed. In preferredembodiments, the nucleic acid with a known sequence can be derived froman organism with a stable and non-variable genome. The control nucleicacid can thus be all or part of a genome from such an organism. Inpreferred embodiments, any suitable organism with a genome that isstable and non-variable can be used to generate a control nucleic acid.In a more preferred embodiment, the control nucleic acid is from abacteriophage genome. For example, the bacteriophage Phi X 174 is knownin the art to contain a stable and highly non-variable genome. However,it will be apparent that other suitable organisms, including but notlimited to bacteriophage organisms, may be utilized to generate acontrol nucleic acid. As an alternative, the control nucleic acid can bea synthetic nucleic acid, preferably of a defined sequence that isknown.

In some embodiments where a flow cell is used, the plurality of arraysare present in a flow cell having a plurality of fluid channels. Inpreferred embodiments, each fluid channel of the plurality of fluidchannels comprises a plurality of tiles. Thus, for example the controlnucleic acid can be provided in different tiles within a channel of theflow cell. Additionally, the control nucleic acid can be provided in oneor more different fluid channels of the flow cell. In a preferredembodiment, the entire area of a tile comprises replicates of thecontrol nucleic acid. In some such preferred embodiments, a plurality ofsuch tiles are included in the flow cell.

Example 1 Real Time Analysis System

This example demonstrates how a Real Time Analysis (RTA) system performsprocessing and data analysis in the background of data acquisitionduring DNA sequencing run. At a basic level, the RTA can be classifiedas a state machine. In the exemplary methods shown in FIG. 1 , the statemachine monitors a file system to determine when a specimen is ready toadvance to the next state. In this example, the conditions to advancethe state for a specimen and subsequent actions to perform on thatspecimen are listed in the following table.

Condition to advance state Action to perform Ready to pre-process Cycle1 Pre-process template cycles Ready to Calculate Template Calculatetemplate (either using default offsets or generate offset-less template)Ready to Register and Extract Register and extract cycle Cycle X Readyto Calculate Color Matrix Calculate color matrix Ready to CalculatePhasing Calculate phasing parameters Ready to Base Call Cycle X Basecall and correct for color and phasing, apply adaptive matrix Ready toQuality Score Cycle X Quality score base calls

For example, a specimen will be ready to register and extract cycle 5if: a) a template has been produced for that specimen and b) four imagefiles (one TIF file for each channel) exist for that specimen for cycle5.

The output of each processing step is a file, which is then used as atrigger for a subsequent processing step. For example, the output of theextraction step is a cluster intensity file (shown as “.cif” in FIG. 1), which can then be used as a trigger for the base calling step. Theoutput of a base calling step is a base call file (shown as “.bcl” inFIG. 1 ), which is then used as a trigger for the quality scoring step.If a specimen is ready to advance in more than one state (e.g., ready toextract cycle 20, base call cycle 16 and quality score cycle 13), stepsearlier in the data analysis process are given higher priority. In thehypothetical situation described above, extraction will take priorityover base calling, and base calling will take priority over qualityscoring.

In this example, the primary input required is one or more image filesfrom a sequencing run. The state machine also can use default offsets(in the form of a DefaultOffsets file), for template generation. TheDefaultOffsets file contains the relative transformation (shift, scale,skew) for the different channels relative to the A channel. If aDefaultOffsets file does not exist, the system will estimate the offsetsfrom its first run and save the DefaultOffsets file. Also, the systemwill save a new DefaultOffsets file if it detects that there has been achange in the offsets (due to physical camera alignment or filterchanges, for example).

The system can also read files which describe the details of the currentrun. In particular, the system looks in this file for: a) the name ofthe run, b) the number of cycles in the run, c) whether or not the runis paired end and d) which specimen (tiles) are being imaged for a readprep. In addition, the system can utilize a configuration file which isan xml file with key-value pairs for certain settings determined by theuser and by the needs of the sequencing equipment.

Finally, the system can be called with command-line arguments. Thesearguments can include: Number of threads to use (defaults to 2); CopyImages flag (defaults to false); Call Bases (defaults to true); ShowGUI(defaults to true); Read Number; Number of cycles; Instrument name;Cycle Type (readPrep, read1, read2, read).

The primary output files produced by the system are QSEQ files. Eachspecimen that is analyzed will produce a QSEQ file that contains thebase call and associated quality score for every cycle, every cluster.One QSEQ file is produced for each read in a paired-end run. The QSEQfiles can then be used as input to alignment software, which aligns thereads to a reference genome. QSEQ files are typically text files.

Alternatively, the system can also output intensity and noise files, inthe form of cluster intensity files and cluster noise files (cif andcnf). A single cif and cnf file is generated for every cycle for everytile. The CIF and CNF files can be used as input to sequence analysissoftware for off-line base calling.

Example 2 Template Generation: Signal Finding

This example demonstrates how template generation is performed during aDNA sequencing run in a flow channel having a plurality ofclosely-spaced, high-density microarrays. An initial step in theprocessing of image data from a sequencing instrument is the generationof templates for each array (tile). A template defines the positions ofeach feature (area of clonally amplified nucleic acid) in a tile, and isused as a reference for the subsequent registration and intensityextraction steps. In this example, the templates are defined in acoordinate system relative to the A image of the first cycle.

Template Generation

In this example, template generation utilized the first four cycles ofimage data. Once the last cycle for a tile was imaged, its template wasgenerated. The basic steps in template generation were:

-   -   a) find spots in all 16 images (4 channels per cycle for 4        cycles), along with the intensity and noise value for each spot;    -   b) merge the spots from the 4 channels of each cycle using        pre-defined offsets between channels in order to create a        preliminary template. The intensity and noise values are used to        give spots with higher signal-to-noise priority;    -   c) use the preliminary template from the cycle with the most        signals (image spots) identified in channel A and C, and        register spots from all other cycles to the template;    -   and    -   d) merge the spots from all images using the pre-defined offsets        and the offsets determined from registration of the template        cycles. Save the template as a locations file.

Pre-Processing

It was also beneficial to do whatever pre-processing possible for eachtemplate cycle as the images became available so as to minimize theprocessing time required during template generation. Towards that end,the following steps were performed for each template cycle:

-   -   i) Find spots in each of the 4 channels;    -   ii) Determine intensity and noise values for all the spots in        each image;    -   iii) Save the positions of the spots for each image;    -   iv) Save the intensities for the spots;    -   v) Merge the spots from the 4 channels using pre-defined        offsets; and    -   vi) Save the preliminary template.

In this example, preprocessing steps were subsumed within steps (a) and(b) of the basic image processing method described above.

Offsets and Offset-Less Template Generation

The system checked to ensure that the DefaultOffsets were valid on aper-tile basis. This was done by looking at the relative offsetsresulting from template cycle registration using the preliminarytemplate. If those relative offsets differed from the DefaultOffsets bygreater than 1 pixel, then offset-less generation was performed for thetile. Offset-less template generation works by finding spots in eachimage, registering channel A against channel C (utilizing the cross-talkbetween A and C), then registering and merging spots across all cyclesto one template. Each of these steps is described with respect to FIG.2B as follows:

A golden cycle g was determined, which was the template cycle with themost spots in channels A and C. The silver cycle, s, was the runner upcycle. Image A from the golden cycle (Ag) became the frame of reference.Everything else was registered against it, directly or indirectly. Theregistration and merge steps were performed as follows:

-   -   Register Cg against Ag;    -   Merge Ag and Cg to form reference (A+C)g;    -   Merge As and Cs to (A+C)s;    -   Register against (A+C)g;    -   Register Gg and Tg against (A+C)s;    -   Register all other images (An, Cn, Gn, Tn) against (A+C)g;    -   Merge spot lists together at the end of the procedure;    -   Spots with higher signal-to-noise are saved.

As a result of the above template generation process, new offsets weregenerated and an offsets file was created for that tile. Once thetemplate, and corresponding offsets file, were generated, each cycle'simages were registered and extracted against the template. Since, at thetime the actual template was generated, 4 cycles had already beenimaged, those cycles had to be registered and extracted in order tocatch up to the imaging. Implementation of the current templategeneration methods allowed registration of images against the templateto substantially lag behind image acquisition and then catch up. Inpractice, the system fell behind one or two cycles due to templategeneration, but usually caught up to real time by cycle 4 or 5.

In addition to the foregoing, a new DefaultOffsets file was generated bycombining the new offset information produced for each tile. Forexample, after two-thirds of the tiles had their templates generated,the system went back and loaded all of the offsets files that had beengenerated, and calculated a median offset. If this median offsetdiffered by more than 0.15 pixels from the DefaultOffsets (or if therewere no DefaultOffsets in the first place) then the system saved theDefaultOffsets file and restarted template generation for all tilesusing the new DefaultOffsets.

Comparison of Improved Procedure with Standard Procedure

FIGS. 3A and 3B illustrate advantages of the improved templategeneration procedure set forth herein as compared to previously-appliedprocedures. For example, in the previously-applied procedure depicted inFIG. 2A, the overlap between certain features can be small enough that afeature might be mis-registered. For example, as shown in the left-mostVenn diagram in FIG. 3A, the overlap between the feature shown as G1 andthe area covered by other features shown as A1+C1+A2+C2 is small enoughthat it is easy to mis-register. However, the shared area becomesgreater when G1 is registered against A2+C2 using the procedure depictedin FIG. 2B as is shown in the right-most Venn diagram in FIG. 3A. Thiseffect becomes even greater in subsequent registration steps, as shownin FIG. 3B (compare left and right Venn diagrams).

Example 3 Template Generation: Signal Selection

This example demonstrates how template generation was performed during aDNA sequencing run. A step in the processing of image data from asequencing instrument is the process of discarding duplicate signals,e.g., two or more signals included in the template produced from thesame feature. In this example, signals were ordered first by detectioncount, then by brightness relative to neighbors.

Spots were ordered first by detection count. A detection count for aparticular spot was defined as the number of cycles where any spot wasdetected within 0.5 pixels of the particular spot. Spots were thenordered by brightness relative to neighbors. For this calculation, thefollowing equation was used:

Ratio=4*Intensity[X,Y]/(NW+NE+SW+SE)

In the above equation, NW was defined as: max(Noise[X,Y], Intensity[X−1, Y−1]). NE, SW and SE are defined similarly. The max was taken toavoid instability if neighboring intensities were less than or equal tozero.

In other experiments, the ClusterDistance radius (distance betweenclusters, which is also referred to herein as “ClusterDistance”) wasdropped from 2.0 pixels to as low as 1.0 pixels, depending on the datadensity. Making either of these changes (ordering spots as describedabove or lowering the ClusterDistance radius) improved the percentage ofclusters meeting the threshold requirements to be characterized as asignal cluster corresponding to a feature (passing filter). In otherwords, either by ordering spots by detection count and then brightnessor by reducing the size of the distance between clusters, increases thepercentage of clusters passing filter (“ClustersPF”). When both changeswere made, the best overall results were achieved, with over 800,000clusters passing filters per mm². Template generation with aClusterDistance radius of 1.5 pixels gave a significant improvement inthe number of clusters passing filters at high density.

Dynamic scaling of ClusterDistance

If ClusterDistance is too small, the result can be a significantpercentage of duplicate signals. A desired percentage of duplicates wasless than 1%. In order to reduce the number of duplicate signalsClusterDistance was sized to accommodate the density of the arrays so asto reduce the number of duplicates. This process involves dynamicscaling for ClusterDistance. In this process, the median spot density(in clusters per mm²) was computed across the template images. If theconfigured ClusterDistance was too small for this spot density, thenClusterDistance value was increased for the tile to potentially reduceduplicates. For densities below 250,000 clusters per mm², a minimumClusterDistance of 2.5 pixels was enforced.

When the ClusterDistance as configured above was set at 1.0 pixels, theresult was only ˜0.3% duplicates amongst clusters passing filter at thehighest densities.

Example 4 Phasing Estimation

This example describes an embodiment wherein a phasing estimation wasperformed to adjust the observed intensities in a way that reduces thenoise created by phased molecules. In this example, it was assumed thata fixed fraction of molecules in each cluster become “phased” at eachcycle, in the sense that those molecules fall one base behind insequencing.

Assuming that p is that fraction, then after cycle 1, a given clusterhas (1−p) percent its molecules on cycle 2, with p percent on cycle 1.After cycle 2, (1−p)² will be on cycle 3, p(1−p) will be on cycle 2, andp² will be on cycle 1. In general, after cycle n, the fraction ofmolecules that will be phased by k cycles will be:

$\begin{pmatrix}n \\k\end{pmatrix}\left( {1 - p} \right)^{n - k}p^{k}$

If p and n are “small” then the intensity contribution from moleculesphased more than 1 cycle (second order terms and higher) is small. Inthat case, the ratio can be taken of intensities of molecules that arephased by one cycle and those that are not phased to get:

$\frac{{{np}\left( {1 - p} \right)}^{({n - 1})}}{\left( {1 - p} \right)^{n}} = {\frac{np}{\left( {1 - p} \right)} \cong {np}}$

To estimate p, the following method was applied for cycles 3 through 12.The first two cycles were ignored as the first cycle is often “T” onlyand this method required a roughly equal number of each base to bepresent in two consecutive cycles.

For channel “A”, all clusters were determined in cycle N for which “A”is not the brightest intensity. Clusters were divided into two groups:clusters where the previous base call in cycle N−1 was “A” and clusterswhere the previous base call was not “A”.

For clusters where the previous call was “A”, it was assumed that eachcluster intensity at cycle N consists of a phasing component p and anoise component n, making the total signal p+n. Each intensity wasnormalized by the intensity of the brightest channel at cycle N to get(p+n)/I and then average for all clusters in the group.

For clusters where the previous call was not “A”, it was assumed thateach cluster intensity at cycle N consists of just a noise component n.Each intensity was normalized to get n/I and then average for allclusters in the group. Phasing at cycle N was set to the differencebetween average (p+n)/I and average n/I.

This process was then repeated for other cycles and determine best fitline. The slope was the phasing for color channel “A”. This process wasthen repeated for all color channels. Phasing across color channels wasaveraged to obtain phasing parameter for entire run.

The methods and systems provided herein also estimated the pre-phasingparameter q the same way but used cycles 2 through 11 and comparedintensities against cycle N+1 rather than cycle N−1. If a control lanewas specified, only tiles from that lane were used to calculate theseparameters.

Once the phasing and pre-phasing parameters were calculated, a phasingmatrix was created to model phasing effects. This was done by creatingan N×N matrix where N is the total number of cycles. Rows representcycles and columns represent template termination position. Withoutphasing or pre-phasing, termination position was expected to match thecycle number at any given cycle. In other words, the probability thatthe termination position at cycle n is equal to position n is 1, and 0elsewhere. For 3 cycles, the matrix would look like:

1 0 0 0 1 0 0 0 1

With phasing and pre-phasing, there are now three probabilities toconsider. First, the probability that the position at cycle n is equalto n−1 is p, where p is the phasing parameter previously calculated.Second, the probability that the position at cycle n is equal to n+1 isq, where q is the pre-phasing parameter. Third, the probability that theposition matches the cycle number, i.e. position at cycle n is equal ton, is now 1−p−q. Thus, the probability that the position at cycle i isj, or P(i,j), is the sum of 3 contributing probabilities:

p*P(i−1, j): the probability that phasing occurred; position did notchange from previous cycle.

(1−p−q)*P(i−1,j−1): the probability that no phasing or pre-phasingoccurred; position incremented 1 from previous cycle.

q*P(i−1,j−2): the probability that pre-phasing occurred; positionincremented 2 from previous cycle.

With this definition, the following phasing matrix was built.

Pos j = 1 Pos j = 2 Pos j = 3 . . . Pos j = N 1 (1 − p − q) q 0 . . . 02 p * P(1, 1) (1 − p − q) * q * P(1, 1) + . . . 0 P(1, 1) + (1 − p −q) * P * P(1, 2) P(1, 2) + P * P(1, 3) . . . . . . . . . . . . . . . . .. N p * P(N − 1, 1) (1 − p − q) * q * P(N − 1, j − 2) + . . . q * P(N −1, N − 2) + P(N − 1, j − 1) + (1 − p − q) * (1 − p − q) * P * P(N − 1,j) P(N − 1, j − 1) + P(N − 1, N − 1) + P * P(N − 1, j) P * P(N − 1, N)

To phase correct intensities for a given cycle, the inverse of thephasing matrix was taken and the matrix row corresponding to the cyclewas extracted. Probabilities less than a threshold of 0.01 were set to0, thus creating a phasing window which was applied to the vector ofobserved intensities values. Finally, the vector of actual intensitiesfor cycles 1 through N was the product of phasing matrix inverse andobserved intensities for cycles 1 through N.

I _(a) =M ⁻¹ ×I _(o)

Example 5 Real Time Metrics

This example describes an embodiment wherein real time metrics of thequality of a sequencing run are described. The system in this embodimentprovided real-time metrics in two forms. The first was the Status.xmlpage within the Data folder. This page offered several views. The RunInfo view showed general run information such as run time and settings.The Tile Status view displayed the current processing state and cycle ofeach tile. The Charts view displayed average metrics for each tilevisually across the physical flow cell. Average metrics included clusterdensity, % cluster passing filter, and intensity, focus quality, and %quality score greater than Q30 by color and cycle. The Cluster Densityview displayed a box plot representing the distribution of clusterdensity by lane. The data in this plot was populated as soon as thefirst cycle was processed. However, during template generation, thenumber of clusters in the temporary reference was only an estimate(actually, an under-estimate) of the number of clusters in the finaltemplate, which was generated in cycle 4. Therefore, the values changedfrom cycle 1 through cycle 4. However, they did not change after cycle4. The data points used to generate this plot were written to file“NumClusters by lane.txt” in the Data/reports directory.

The Intensity & Focus Quality view displayed two box plots. The firstplot represented the distribution of 90th percentile raw intensityvalues grouped by cycle and color channel. This plot provided anindication of the intensity decay. The data points used to generate thisplot were written to file “Intensity by Color and Cycle.txt”. The secondplot represented the distribution of focus quality grouped by cycle andcolor channel. The data points used to generate this plot were writtento file “FWHM by Color and Cycle.txt”.

Another way real-time metrics were reported was through the .BRO files(by default, this was enabled, but can be disabled through the configfile). These were XML files that contain statistics at an image-level. Asingle .bro file contained all of the statistics for all of the tiles ina given lane for a given cycle.

The above description discloses several methods and systems of thepresent invention. This invention is susceptible to modifications in themethods and materials, as well as alterations in the fabrication methodsand equipment. Such modifications will become apparent to those skilledin the art from a consideration of this disclosure or practice of theinvention disclosed herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments disclosed herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention.

All references cited herein including, but not limited to, published andunpublished applications, patents, and literature references, areincorporated herein by reference in their entirety and are hereby made apart of this specification. To the extent publications and patents orpatent applications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,”“containing,” or “characterized by,” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps.

1. (canceled)
 2. A method for creating a template of nucleic acidcluster locations on a flow cell, comprising: obtaining fluorescentemission signals from nucleic acid clusters on the flow cell, wherein anucleic acid cluster comprises a plurality of nucleic acid moleculesattached to the flow cell, and wherein the plurality of the nucleic acidmolecules comprise fluorescently labeled nucleotides; identifying aplurality of candidate cluster locations on the flowcell from theobtained fluorescent emission signals; and determining whether to keep acandidate cluster location, discard a candidate cluster location, ormerge a candidate cluster location with another location based on theobtained fluorescent emission signals, thereby generating a template ofcluster locations on the flow cell.
 3. The method of claim 2, whereinsaid determination is based on clusters of fluorescent emission signalsassociated with neighboring candidate cluster locations of saidcandidate cluster location.
 4. The method of claim 2, wherein saiddetermination comprises ordering said candidate cluster locations bytheir respective detection counts, wherein a detection count of acandidate cluster location is a number of times a fluorescent emissionsignal is detected within a predefined radius of the candidate clusterlocation during multiple sequencing cycles.
 5. The method of claim 4,wherein the predefined radius is adjusted based on a density ofcandidate cluster locations on the flow cell.
 6. The method of claim 4,wherein the predefined radius is measured from a center of the candidatecluster location, from an edge of the candidate cluster location, orfrom a point inside the candidate cluster location.
 7. The method ofclaim 4, wherein said plurality of candidate cluster locations arefurther ordered by their respective highest fluorescent emission signalintensities relative to highest fluorescent emission signal intensitiesassociated with their neighboring candidate cluster locations.
 8. Themethod of claim 2, wherein said determination comprises ordering saidplurality of candidate cluster locations by: their respective highestfluorescent emission signal intensities, their respective highestfluorescent emission signal intensities relative to a backgroundintensity or a noise intensity, or their respective highest fluorescentemission signal intensities relative to highest fluorescent emissionsignal intensities associated with their neighboring candidate clusterlocations.
 9. The method of claim 2, wherein said determinationcomprises ordering said plurality of candidate cluster locations bydistance to their respective nearest neighboring candidate clusterlocations.
 10. The method of claim 9, wherein the distances are measuredfrom a center of a candidate cluster location to a center of its nearestneighboring candidate cluster location, from an edge of a candidatecluster location to an edge of its nearest neighboring candidate clusterlocation, between the respective outer-most identifiable points of acandidate cluster location and its nearest neighboring candidate clusterlocation, or from a point inside a candidate cluster location to anotherpoint inside its nearest neighboring candidate cluster location.
 11. Themethod of claim 2, wherein said determination comprises ordering saidplurality of candidate cluster locations by their respective chastities,wherein a chastity of a candidate cluster location in a template cyclerelates to a comparison of the highest and the second highestfluorescent emission signals detected in the template cycle at thecandidate cluster location by different detection channels havingdifferent wavelength ranges.
 12. The method of claim 11, wherein saiddetermination is further based on preliminary base calls at saidplurality of candidate cluster locations.
 13. The method of claim 2,further comprising processing the cluster of fluorescent emissionsignals associated with each of said plurality of candidate clusterlocations to produce a clump of processed fluorescent emission signalsassociated with each of said plurality of candidate cluster locations14. The method of claim 13, wherein determining whether to keep acandidate cluster location, discard a candidate cluster location, ormerge a candidate cluster location with another location is based on theassociated clump of processed fluorescent emission signals.
 15. Themethod of claim 13, wherein processing the cluster of fluorescentemission signals associated with each of said plurality of candidatecluster locations comprises discarding fluorescent emission signalsamong the cluster of fluorescent emission signals that are not within anexpected size measure.
 16. The method of claim 13, wherein processingthe cluster of fluorescent emission signals associated with each of saidplurality of candidate cluster locations comprises selecting thefluorescent emission signal among the cluster of fluorescent emissionsignals that has the highest intensity.
 17. A system for determiningnucleotide sequences of nucleic acid clusters in a flow cell, the systemcomprising: an optical system for detecting fluorescent emissions; anon-transitory memory configured to store executable instructions; and ahardware processor in communication with the optical system and thenon-transitory memory, the hardware processor programmed by theexecutable instructions to perform the process of claim
 2. 18. Thesystem of claim 17, wherein the nucleic acid sequencer is configured toacquire images of the flow cell.
 19. The system of claim 17, wherein thehardware processor is configured to control the nucleic acid sequencerto perform sequencing reactions on the nucleic acid clusters todetermine the nucleotide sequences in each cluster.